{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import metrics\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "import seaborn as sns  \n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('../data_files/1.Titantic_data/train.csv')\n",
    "test = pd.read_csv('../data_files/1.Titantic_data/test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.6+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 11 columns):\n",
      "PassengerId    418 non-null int64\n",
      "Pclass         418 non-null int64\n",
      "Name           418 non-null object\n",
      "Sex            418 non-null object\n",
      "Age            332 non-null float64\n",
      "SibSp          418 non-null int64\n",
      "Parch          418 non-null int64\n",
      "Ticket         418 non-null object\n",
      "Fare           417 non-null float64\n",
      "Cabin          91 non-null object\n",
      "Embarked       418 non-null object\n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 36.0+ KB\n"
     ]
    }
   ],
   "source": [
    "test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percent of missing \"Cabin\" records is 19.87%\n"
     ]
    }
   ],
   "source": [
    "print('Percent of missing \"Cabin\" records is %.2f%%' %((train['Age'].isnull().sum()/train.shape[0])*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    714.000000\n",
       "mean      29.699118\n",
       "std       14.526497\n",
       "min        0.420000\n",
       "25%       20.125000\n",
       "50%       28.000000\n",
       "75%       38.000000\n",
       "max       80.000000\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set()\n",
    "sns.set_style('ticks')\n",
    "\n",
    "# 缺失值处理：年龄Age字段\n",
    "train_age=train[train['Age'].notnull()]\n",
    "\n",
    "# 年龄数据的分布情况\n",
    "plt.figure(figsize=(12,8))\n",
    "\n",
    "plt.subplot(121)\n",
    "train_age['Age'].hist(bins=80)\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Num')\n",
    "\n",
    "plt.subplot(122)\n",
    "train_age.boxplot(column='Age',showfliers=True,showmeans=True)\n",
    "\n",
    "train_age['Age'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            891 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.6+ KB\n"
     ]
    }
   ],
   "source": [
    "# 要对缺失值处理\n",
    "train['Age']=train['Age'].fillna(train['Age'].mean())\n",
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percent of missing \"Cabin\" records is 77.10%\n"
     ]
    }
   ],
   "source": [
    "print('Percent of missing \"Cabin\" records is %.2f%%' %((train['Cabin'].isnull().sum()/train.shape[0])*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop(['Cabin'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 11 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            891 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(4)\n",
      "memory usage: 76.6+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "S    644\n",
       "C    168\n",
       "Q     77\n",
       "Name: Embarked, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.countplot(x='Embarked',data=train,palette='Set1')\n",
    "plt.show()\n",
    "train['Embarked'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.Embarked = train.Embarked.fillna('S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 11 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            891 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Embarked       891 non-null object\n",
      "dtypes: float64(2), int64(5), object(4)\n",
      "memory usage: 76.6+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc61439a190>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_survived=train[train['Survived'].notnull()]\n",
    "\n",
    "# 用seaborn绘制饼图，分析已知存活数据中的存活比例\n",
    "sns.set_style('ticks') # 十字叉\n",
    "plt.axis('equal')       #行宽相同\n",
    "train_survived['Survived'].value_counts().plot.pie(autopct='%1.2f%%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "女性存活率74.20%,男性存活率18.89%\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 男性和女性存活情况\n",
    "train[['Sex','Survived']].groupby('Sex').mean().plot.bar()\n",
    "\n",
    "survive_sex=train.groupby(['Sex','Survived'])['Survived'].count()\n",
    "\n",
    "print('女性存活率%.2f%%,男性存活率%.2f%%' %\n",
    "     (survive_sex.loc['female',1]/survive_sex.loc['female'].sum()*100,\n",
    "      survive_sex.loc['male',1]/survive_sex.loc['male'].sum()*100)\n",
    "     )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc6142e9d50>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 年龄与存活关系\n",
    "fig,ax=plt.subplots(1,2,figsize=(18,8))\n",
    "\n",
    "sns.violinplot('Pclass','Age',hue='Survived',data=train_age,split=True,ax=ax[0])\n",
    "ax[0].set_title('Pclass and Age vs Survived')\n",
    "\n",
    "sns.violinplot('Sex','Age',hue='Survived',data=train_age,split=True,ax=ax[1])\n",
    "ax[1].set_title('Sex and Age vs Survived')\n",
    "\n",
    "# 老人和小孩存活率\n",
    "plt.figure(figsize=(18,4))\n",
    "# 年龄都转换成整数\n",
    "train_age['Age']=train_age['Age'].astype(np.int)\n",
    "average_age=train_age[['Age','Survived']].groupby('Age',as_index=False).mean()\n",
    "\n",
    "sns.barplot(x='Age',y='Survived',data=average_age,palette='BuPu')\n",
    "# plt.grid(linestyele='--',alpha=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc6121279d0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 筛选出有无兄弟姐妹\n",
    "sibsp_df = train[train['SibSp']!=0] # 有兄弟姐妹\n",
    "no_sibsp_df = train[train['SibSp']==0] # 没有兄弟姐妹\n",
    "\n",
    "# 筛选处有无父母子女\n",
    "parch_df = train[train['Parch']!=0] # 有父母子女\n",
    "no_parch_df = train[train['Parch']==0] # 没有父母\n",
    "\n",
    "plt.figure(figsize=(12,3))\n",
    "plt.subplot(141)\n",
    "plt.axis('equal')\n",
    "sibsp_df['Survived'].value_counts().plot.pie(labels=['No Survived','Survived'],autopct='%1.1f%%',colormap='Blues')\n",
    "\n",
    "plt.subplot(142)\n",
    "plt.axis('equal')\n",
    "no_sibsp_df['Survived'].value_counts().plot.pie(labels=['No Survived','Survived'],autopct='%1.1f%%',colormap='Blues')\n",
    "\n",
    "\n",
    "plt.subplot(143)\n",
    "plt.axis('equal')\n",
    "parch_df['Survived'].value_counts().plot.pie(labels=['No Survived','Survived'],autopct='%1.1f%%',colormap='Reds')\n",
    "\n",
    "plt.subplot(144)\n",
    "plt.axis('equal')\n",
    "no_parch_df['Survived'].value_counts().plot.pie(labels=['No Survived','Survived'],autopct='%1.1f%%',colormap='Reds')\n",
    "     \n",
    "\n",
    "# 亲戚多少与是否存活有关吗？\n",
    "fig,ax=plt.subplots(1,2,figsize=(15,4))\n",
    "train[['Parch','Survived']].groupby('Parch').mean().plot.bar(ax=ax[0])\n",
    "train[['SibSp','Survived']].groupby('SibSp').mean().plot.bar(ax=ax[1])\n",
    "\n",
    "train['family_size']=train['Parch']+train['SibSp']+1\n",
    "train[['family_size','Survived']].groupby('family_size').mean().plot.bar(figsize=(15,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure()\n",
    "fig.set(alpha=0.2)\n",
    "\n",
    "plt.subplot2grid((2,3),(0,0))\n",
    "train.Survived.value_counts().plot(kind='bar')\n",
    "plt.title('Survived')\n",
    "plt.ylabel('num')\n",
    "\n",
    "plt.subplot2grid((2,3),(0,1))\n",
    "train.Pclass.value_counts().plot(kind='bar')\n",
    "plt.title('Pclass')\n",
    "plt.ylabel('num')\n",
    "\n",
    "plt.subplot2grid((2,3),(0,2))\n",
    "plt.scatter(train.Survived,train.Age)\n",
    "plt.ylabel('Age')\n",
    "plt.grid(b=True,which='major',axis='y')\n",
    "plt.title('Age')\n",
    "\n",
    "plt.subplot2grid((2,3),(1,0),colspan=2)\n",
    "train.Age[train.Pclass == 1].plot(kind='kde')\n",
    "train.Age[train.Pclass == 2].plot(kind='kde')\n",
    "train.Age[train.Pclass == 3].plot(kind='kde')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Density')\n",
    "plt.title('Distribution of passenger ages by Pclass')\n",
    "plt.legend(('first','second','third'),loc='best')\n",
    "\n",
    "plt.subplot2grid((2,3),(1,2))\n",
    "train.Embarked.value_counts().plot(kind='bar')\n",
    "plt.title('Embaked')\n",
    "plt.ylabel('num')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc61039b090>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(1,2,figsize=(15,4))\n",
    "train['Fare'].hist(bins=70,ax=ax[0])\n",
    "train.boxplot(column='Fare',by='Pclass',showfliers=False,ax=ax[1])\n",
    "\n",
    "fare_not_survived=train['Fare'][train['Survived']==0]\n",
    "fare_survived=train['Fare'][train['Survived']==1]\n",
    "# 筛选数据\n",
    "\n",
    "average_fare=pd.DataFrame([fare_not_survived.mean(),fare_survived.mean()])\n",
    "std_fare=pd.DataFrame([fare_not_survived.std(),fare_survived.std()])\n",
    "\n",
    "average_fare.plot(yerr=std_fare,kind='bar',figsize=(15,4),grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc61027c850>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18,4))\n",
    "train_age['Age']=train_age['Age'].astype(np.int)\n",
    "average_age=train_age[['Age','Survived']].groupby('Age',as_index=False).mean()\n",
    "\n",
    "sns.barplot(x='Age',y='Survived',data=average_age,palette='BuPu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc6142be610>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train[['Pclass','Survived']].groupby('Pclass').mean().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Sex and Age vs Survived')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(1,2,figsize=(18,8))\n",
    "\n",
    "sns.violinplot('Pclass','Age',hue='Survived',data=train_age,split=True,ax=ax[0])\n",
    "ax[0].set_title('Pclass and Age vs Survived')\n",
    "\n",
    "sns.violinplot('Sex','Age',hue='Survived',data=train_age,split=True,ax=ax[1])\n",
    "ax[1].set_title('Sex and Age vs Survived')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot('Embarked', 'Survived', data=train, color=\"teal\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Cg4PleRwdHcnMzJTL9u/fr9E77vNK26eUIAi6JxKLUKGUSiUrV65k4cKFZGVlUbNmzSLL6tWrx6pVq3QYrSAI2qDTxBIQEMCGDRsIDg6mWbNmXLt2jQULFmh8yeR/ERVXJlQ9p0+flv93dnaWr1Tyh7AtrEwQhFeDzh43Lqm9w/NtGkR7B0EQhOpDJ4mlNO0dnm/TINo7CIIgVB86qQorTXuH59s0iPYOgiAI1UelJxbR3kEQBOHVVumJpbTtHZ5v0yDaOwiCIFQflZ5YStve4fk2DaK9gyAIQvVRZdqxFNemQbR3EARBqD50nliKau/wItHeQRAEoXoQ3eYLgvBSPD09ef/994G8qu6ff/4ZEIO4vc5EYhEE4aUtXLgQgK1bt9KlSxfRqPk1JxKLIAhaV9pGzcnJyURERGj8iXZn1Z/O77EIglD9LVu2DMhr/Dx//vxSN2ourt1ZaGgosbGxJW77ypUrLxm98KL8YxoXF1eu5UViEQThpQQGBqJWq/Hy8kKSJJYsWULPnj1LtWxx7c4cHR3/6p1jb0SR62jXrl25Y3+tleKYRkQUPU9xyp1Ybt++zbJly7h9+zbp6ekASJKEQqEgNDS0vKsVtCxbpcbQQK/U85f1vJZ1/ULVExYWxokTJ4iPj2fhwoWEhYWhUqlo0aJFqZa3t7eXv4B8fX1ZtGgRo0ePLlWjZtHu7NVU7sQybdo0evXqxbx58zA2NtZmTIIWGRro0f+zw6We/8+zqzG3fxvrNmOpoWegUVbYeoLX+L10jILuHD9+XL7COHr0KAsXLiQ9PZ01a9awa9euEpdPT09HrVbLr8+cOUPLli3L1KhZePWUO7HEx8czdepUFAqFNuMRdCwnK4WazXqJ8/qaWL9+PTt37qRFixYcP34cgBYtWnD79u1SLZ+QkMDkyZPJzMwE8qpO/vGPf4hGza+5cieWAQMGEBwcjK+vrzbjEXTM0qEdKZFXsXRw1nUoQiVITEykefPmAPKPCYVCUeofFvXr1ycoKEgewG3hwoXUrl0bEI2aX2flTizjx49n2LBhfP311wVGctyzZ89LBybohk2T7oT/upHE+2fQMzLXKKvf8UMdRSVUlLfeeovDhw8zYMAAedr3339P69atdRiVUN2VO7FMmTIFBwcHevbsiZGRkTZjEnQo+sq36JvYYG73FsoX7rEIr565c+cyduxYDh48SHp6OmPHjuXhw4d88803ug5NqMbKnVhu3brFxYsXMTQ01GY8go5lPouiSe9FKJTiSfTXQePGjTl+/DhnzpyhW7du2Nvb061bN8zMzHQdmlCNlfvbw8XFhbCwMFq2bKnNeAQdM7FpRFbKE4yt6pY8s/BKMDExwcfHR9dhCK+QcicWBwcH/P396dmzZ4F7LFOnTn3pwATdMDC1IfLiNsztHAvcY6nVvLeOohIqyogRIwq9UW9oaIidnR09e/bE09NTB5EJ1Vm5+wrLzMykW7duqFQqYmJiNP6E6ktSqzCr3RIpV01OxjONP+HV0759eyIjI3F1dcXX1xdXV1eioqJwdHSkZs2azJkzh23btuk6TKGaKfcVyxdffKHNOIQqwq7tO7oOQahEv/76Kzt27KBx48bytP79+zN79mwOHDhAr169mDZtGh988IEOoxSqm3InlvDw8CLL6tevX97VCjqWnVb0mBmGZjWLLBOqpwcPHhT4vNarV4+HDx8C0Lp1azGOilBm5U4sPXv2RKFQIEmSPC2/rvbWrVsvH5mgE3+eWVlkWbN+KyoxEqEyuLq68vnnnzNlyhTs7OyIiYlh/fr1cieEd+7cwdbWVsdRCtXNS3VC+by4uDgCAgLk8ReE6unF5JGTmULCvR8xsWmko4iEirR8+XIWL15M3759ycnJQV9fn169erF8+XIADAwMWLNmjY6jFKobrQ30ZWtry9y5c/nyyy+1tUqhCtA3tsC2lS/xt4/rOhShAlhbW7N27Vpu3LjBL7/8wsGDB6lTpw79+/cH4M033+Ttt9/WcZRCdaPVVnAPHjwgIyNDm6sUqgBVWhySWqXrMIQKkpiYSHBwMEFBQdy+fRsXFxfmzp2r67CEaqzcieXF598zMjK4f/8+H3/8sVYCE3Qj/LdNwF/nNVedTXZKLDZNe+guKEHrVCoVp0+f5tChQ/zyyy+88cYb9O3bl8jISL766qsCbdMEoSzKnViGDh2q8drExIQWLVrQsGHDl41J0CHL+u01Xiv1DDGytMfQvOQbuE+fPmXmzJk8fvwYQ0NDGjRowJIlS7CxseHatWssWLCA1NRUed780QHzy57vXv1V+mLTxmBo2h5Qzd3dHYVCwaBBg5g8eTJvvfUWAPv27dPaNoTXV7kTS9++fTl06BC3bt2SRxo8ffo0ACtXFv1kkVC1WdRtS3JECFnJ0eTmZAGQGvs/AOydhhe7rEKhYNy4cbi5uQGwYsUKVq9ezdKlS5kxYwZffPEFdnZ2eHl5sX37dtatW0dubq5c5uLiwqZNm1i9evUr1U6qrIOtFUbbA6o1b96cK1eucP36dRo0aICDgwNWVlZa3Ybw+ip3Ypk9eza3b9+me/fu1KpVS5sxCToUe/1fZCVHY1anFfovdOlSEmtrazmpALRt25Z9+/YRGhqKkZERLi4u8hC2Z8+eBdAoAxg+fDheXl4FEktycjLJycka00QvD+X37bffEhkZSVBQEN988w1Lly6lc+fOpKenk5OTo+vwhGqu3Inl559/5qeffhLjVb9i0uLu0shzNnoGJi+1ntzcXPbt24enpyfR0dHUravZqaUkSSQlJRUos7GxITc3l6SkJI3x0Xfv3k1AQMBLxSRoqlevHhMnTmTixImEhIRw+PBhlEolvr6+DB48mJkzZ+o6RKGaKndisbe3Jzs7u1zLlqYuvrD69le9Lr4q0De2Rsp9+V+sf//73zE1NWXUqFH8+OOPL72+9957j4EDB2pMi4mJYeTIkS+9biGvt3IXFxfmzZvHjz/+SFBQkK5DEqqxcrdjGTBgAB9//DFHjx7lwoULGn8lya+LP3nyJMHBwdSvX5/Vq1fL9e0LFizg5MmTuLi4sHr1aoBiywTtsXRwJurybpIjr5Ief1/jr7RWrFjBo0eP+Oqrr1Aqldjb2xMVFaUxj0KhwNraukBZYmIiSqVS42oFwNLSEgcHB40/Ozu7l9tZoQAjIyP69evH9u3bdR2KUI2V+4rln//8J0CBBpEKhYKffvqp2GVLUxcPmvXtoi6+ciT9+RsA8bdPaExXKBQ08pxd4vJffvkloaGhbN26VR4EztHRkczMTEJCQuRk4OHhUaDMxcWF/fv306dPH23ukkDFPZmWX/vw4MEDABYuXMiqVauwsbGhefPmNGvWDKUy7/frypUrad68+UvFIFQP5U4s+U+Avazi6uKfr28XdfGV402vz8u97L179/j6669p2LAhw4fnPUHm4ODAxo0bWblyJQsXLiQtLQ2AcePGAaBUKuWy56s4Be2qqCfT8msf6tWrh5eXF3Xr1mX16tUsW7YMgP3794vRKF9DOh9/VtTFvzqaNm3KnTt3Ci1zdnYmODiYiIgIvLy8sLGxKVAmVD/5tQ/5T/u1bNmSH374odTLixqGV5NOE0t+XfyWLVsKrYt/vr69LHXx4kk1QdCN4OBgvL295dfvvvsuarWarl27MnnyZLl6NF9xNQyhoaHExsaWuM0rV668XNBCAfnHNC4urlzL6yyxlFQX/2J9u6iLF4Sqz9jYmFGjRgF5bZXs7e1JTU1lxowZbNy4kU8//VRj/uJqGBwdHeXeGdgbUeQ287v4F8qoFMc0/0q0rHSSWEpTF/9ifbuoixeEqmvLli0AzJ8/X75Zb29vD4C5uTlDhw5l586dBZYTNQyvJp0kltLUxZe1TBAE3fjyyy+5e/cugFz78OzZM4yMjDA2NiYnJ4eTJ0/SsmVLXYYpVCKd37wXBKH6yq99yK+yGj9+PI0aNWLcuHEsWLAAhUJBTk4OTk5OTJ06VcfRCpVFJBZBEMotv/Yh/2m/rVu3yklG1C68vrQ2gqQgCIIggEgsgiAIgpaJxCIIgiBolUgsgiAIglaJxCIIgiBolUgsgiAIglaJxCIIgiBolUgsgiAIglaJxCIIgiBolUgsgiAIglaJxCIIgiBolUgsgiAIglaJxCIIgiBolUgsgiAIglaJxCIIgiBolUgsgiAIglaJxCIIgiBolUgsgiAIglaJxCIIgiBolUgsgiBUmIcPHzJs2DB69+7NsGHD+PPPP3UdklAJRGIRBKHCLFy4kBEjRnDy5ElGjBjBggULdB2SUAlEYhEEoUIkJCTwv//9j379+gHQr18//ve//5GYmKjjyISKpq/rAARBeDVFR0dTp04d9PT0ANDT06N27dpER0djY2MDQHJyMsnJyRrLxcTEVHqsgnZVq8Ty8OFDZs+eTVJSEtbW1qxYsYKGDRvqOizhJYnz+vravXs3AQEBhZaFhoYSGxsLgEIBklRwHoUCrly5UpEhvpbyj2lcXFy5lq9WiSW/vtbPz4/Dhw+zYMEC9uzZo+uwhJckzuuryd7entjYWNRqNXp6eqjVap48eYK9vb08z3vvvcfAgQM1louJiWHkyJE4Ojri4OAAgPdDPY5deFRgG94dGtCuXduK3ZFX1d6IIovatWsHQERE0fMUp9rcYxH1ta8mcV5fXTVr1qRly5YcPXoUgKNHj9KyZUu5GgzA0tISBwcHjT87O7sC6/poSFt8OjZAqVQAoFQq8OnYgI+GiKRSXsFr/Mo0vSyqzRVLaeprofA628jISKDsdbeq9Ir9civvr4Gyqsj9KOs+5J8DtVoNVM55fdn918Z5etVjePG85lu0aBGzZ89m06ZNWFpasmLFihK3k7+OF89r/w616N+hVqljEkq2+dN2BaY9f0yLOq8lqTaJpbSKq7MdOXJkJUdTPK/Ty3Udwksr7z7ExcXRoEGDUs+vy/NaFc5TdYnhxfPauHFjDhw4UKbt5NfrV7XP6+usrJ/XapNYSlNfC4XX2WZnZxMeHk7Dhg3lX8ball8vHBgYWOilfHVQGfugVquJi4vD0dER0P151fV50/X2tRXDi+f1ZTg6OhIYGIitrW2B81oVjterprhjWt7zWm0Sy/P1tX5+foXW10Jena2lpWWB5d98881KidPOzk6+4VhdVfQ+PP/Lp6qcV12fN11vXxsxlOUXbXGMjY1xcXEpdp6qcLxeNUUd0/Kc12qTWKB89bVC1SfOqyC8WqrNU2HwV33tyZMnOXDgQLG/VhcsWMDGjRu1st2oqCicnJzkG1jvvvtumeuNizNu3DgOHTqktfWV1tq1a3Fzc8Pd3b3St/28spzX15W233NltWHDBqZPn66z7VdFs2fPZu3ateVaVpvH09PTk99++00r69KWanXFks/T05P4+Hj09PTQ09OjSZMm+Pn5MWzYMJTKvFy5ZMmSUq9r6dKldOrUqch56taty9WrV7US+4YNG3j06BGrV6+Wp23fvl0r6y6LqKgodu7cyZkzZ6hZs2alb1/QjYiICLy8vDA1NQWgRo0aeHt76ziqyvX890e+gQMHin7MtKhaJhaALVu20KlTJ1JSUrh06RL/+Mc/uHHjBl988YVWt5OTk4O+fsmHydLSkkmTJhV6H6AqioqKwtraWiOpVLd90IYX97m057uitl9ZLl++jL6+PlevXuW9996jf//+1eK8a+t45X9/CBXzHqxWVWGFsbCwwMvLi6+++opDhw5x9+5dQPMyNTExkQ8//BAXFxfat2/PiBEjyM3NZcaMGURFRTFhwgScnJzYtm0bERERNG/enAMHDtCtWzfee+89eVpOTo683cePHzNkyBCcnZ356KOPyM3NZfLkydy6dYuuXbtqxJh/qXr+/Hm+/vprjh8/jpOTE76+voBmNUdubi6bNm2ie/fudOzYkZkzZ5KSkgIgx3Ho0CG6deuGm5sbmzdvLvLYpKSkMHPmTDp06ED37t3ZtGkTubm5/Pbbb/j7+/PkyROcnJyYPXs2kPcGmzx5MpaWlly8eJGuXbuyZcsW3Nzc8PT05MiRI/K6z549y4ABA3B2dsbDw4MNGzbIZVlZWUyfPh03NzdcXFwYPHgw8fHxAHz33XfRP1YIAAAgAElEQVR4eXnh5ORUYJ0HDx7E29sbV1dXxo4dK7dTAWjevDn79u2jV69euLi4sHjxYqT/7+NDrVazfPlyOc5//vOfGucrJSWFOXPm0LlzZ7p06cLatWvlas1Tp07x66+/EhAQgJubm8Z+5Ltx4wbDhg3DxcWFzp07s2TJErKzs+XyX375hd69e9OuXTsWLVrEqFGjNKqtitovSZIICAhg7969dOvWjf79+8vv38K8+J5LSkoCYPz48Xz77bca8/bv358ff/yxyHXlc3JyomnTprRs2RJLS0vu3bvHmDFjaN++PZ06dWLLli2FLjdlyhTc3d1p164dI0eO5N69e3LZuXPn8PHxwcnJiS5durBjxw6g6M9hWTz/HtW27777juHDh7Ns2TJcXFzw8vLiv//9L9999x0eHh507NixQJX106dPGTNmDE5OTowaNUrjPbt06VI8PDxwdnZm0KBBhISEFLpdlUrFtGnTmDx5MtnZ2eTm5rJ161Z69OiBm5sbU6dOlc81QFBQEN27dy/x819aFXJMpWqoe/fu0q+//lpguoeHhxQYGChJkiTNmjVL+vLLLyVJkqTVq1dL8+fPl7Kzs6Xs7Gzp8uXLUm5ubqHrCg8Pl5o1aybNmDFDSktLkzIyMuRpKpVKkiRJGjVqlNS5c2fpzp07UlpamjRp0iTps88+kyRJkn7//XepS5cuRca7fv16ed58o0aNkv79739LkiRJBw4ckHr06CE9fvxYSk1NlSZOnChNnz5dI7a5c+dKGRkZ0q1bt6S33npLun//fqHHacaMGdKECROklJQUKTw8XOrVq5e8ncLifN7vv/8utWzZUlq2bJmUlZUlXbx4UWrTpo0UFhYml9++fVtSq9XSrVu3pI4dO0o//vijJEmStG/fPunDDz+U0tPTpZycHOnmzZtSSkqKlJaWJjk5OcnriI2Nle7evStJkiT9+OOPUo8ePaT79+9LKpVK2rhxozRs2DA5nmbNmknjx4+Xnj17JkVGRkpubm7SuXPnJEmSpL1790re3t5SdHS0lJSUJL333nsa5+vjjz+W5s+fL6WlpUnx8fHS4MGDpX379kmSJEn/+c9/pJYtW0p79uyRVCqVlJGRUeBY3Lx5U7p69aqkUqmk8PBwqU+fPtLOnTslSZKkhIQEycnJSTp58qSkUqmkXbt2Sa1atZKPc3H7df78eWngwIHSs2fPpNzcXOn+/ftSbGxsoeejuPfc999/Lw0ZMkSe99atW1L79u2lrKysAut5/r2cm5srhYSESK1bt5Z+++03KSUlRXJ3d5d27NghZWZmSikpKdK1a9ckSSr4vj1w4ICUkpIiZWVlSUuXLpV8fX3lMnd3d+ny5cuSJElSUlKSFBoaKklS8Z/DylTU90f+e+HgwYNSTk6O9OWXX0oeHh7SokWLpKysLOnnn3+W2rZtK6WmpkqSlPcd07ZtW+nSpUtSVlaW9Pe//10aPny4vL6goCApMTFRUqlU0o4dO6ROnTpJmZmZkiT9dTwzMjKkDz74QJo1a5aUk5MjSZIk7dq1Sxo6dKgUHR0tZWVlSfPnz5c+/fRTSZIk6d69exrbXLZsmdSyZctC90eXqv0Vy/Nq167Ns2fPCkzX19cnLi6OqKgoDAwMcHFxQaFQFLuuyZMnY2pqirGxcaHlfn5+NGvWDFNTU6ZOncqJEyfK3Dq1MMHBwbz//vvUr18fMzMzpk2bxrFjxzSuliZNmoSxsTEtWrSgRYsW3L59u8B61Go1x44d47PPPsPc3BwHBwfGjBmjcYVQGlOnTsXQ0JD27dvj4eHB8ePHAXBzc6N58+YolUpatGhB3759uXTpEpB3vJOSknj06BF6eno4Ojpibm4OgFKp5N69e2RmZlK7dm2aNm0KwP79+xk/fjyNGzdGX1+fCRMmcOvWLY1fgB988AGWlpbUrVsXNzc3eb+PHz/O6NGjsbOzw8rKivHjx8vLxMfHc+7cOebMmYOpqSk1a9bk/fff5/vvv5fnqV27Nu+++y76+vqFnm9HR0fatm2Lvr4+Dg4ODBs2jMuXLwNw/vx5mjZtSq9evdDX12f06NHUqvVX6/Di9ktfX5+0tDQePHiAJEk0btyY2rVrF3kuinrPeXl58eeff8qDaB0+fBhvb28MDQ2LXFeHDh1o37498+bN47PPPqNjx46cPXuWWrVq4e/vj5GREebm5rRp06bQ5YcMGYK5uTmGhoZMnjyZ27dvy1fW+vr63L9/n9TUVKysrHjrrbfk6WX9HFaUiRMn4uLiIv/9+9//BsDBwYHBgwejp6eHj48P0dHRTJw4EUNDQzp37oyhoSGPHz+W19OtWzdcXV0xNDTk008/5dq1a0RHRwN556tGjRro6+vj7+9PdnY2Dx8+lJdNTU1l3LhxvPHGG3zxxRfyPZ/9+/fz6aefYmdnh6GhIZMmTeLkyZPk5ORw4sQJjW1OnTpVvq9clVTbeyyFiY2NxcrKqsD0sWPHEhAQgL+/PwDDhg3T+PIpTEmNr55vwFe3bl1UKhVPnz4tR9Sanjx5Qr169eTX9erVIycnh4SEBHna819cJiYmpKenF1jP06dPUalU1K1bVyPO/N5iS8PS0lK+yZu//JMnTwC4fv06q1ev5t69e6hUKrKzs+nTpw+Q94GKiYlh2rRpJCcn4+vry6effoqpqSlr167lm2++Ye7cuTg7OzNr1iwaN25MVFQUy5Yt03jUWJIkYmNj5eNha2ursd9paWnyMXv+fDx/7qKiosjJyaFz587ytNzc3CLnL8zDhw9Zvnw5oaGhZGRkoFar5S/LJ0+eaCyvUCgKbL+o/erYsSMjR45kyZIlREZG0qtXL2bNmiUn4RcV9Z6rVasW3t7eHDlyhEmTJnH06FHWr19f7D79/vvvBe4lRUdH88YbbxS7HOT9aFm7di0nTpwgMTFR/mJ7+vQpFhYWrF+/ns2bN7NmzRqaN2/OZ599hpOTU7k+hy/SVk/YGzduLHCP5bvvvtO455j/I+P5z5uRkZH8vgPN946ZmRlWVlby+3HHjh0cPHiQJ0+eoFAoSE1N1fiOuH79Ojk5OaxZs0YjwUZFRTFx4kSNhKFUKklISCjwfjM1NcXa2rrM+59vxYoVnDx5ksjISIKDg2nWrFm51/W8Vyax3Lhxg9jYWLlXzueZm5sze/ZsZs+ezd27d3nvvfd4++236dixY5HrK+mXVHR0NE+fPiUmJobw8HD09fWpUaMGJiYmZGZmyvOp1WqNDhVLWm/t2rU1fqVHRUWhr69PzZo1y9TXWY0aNTAwMCAqKoomTZrIMdepU6fU60hOTiY9PV1OLtHR0fIVxmeffcaoUaPYvn07RkZG/OMf/5A/NAYGBkyaNIlJkyYRERHB+PHjadSoEUOHDqVLly506dKFzMxMvvrqK+bPn8/evXuxt7dnwoQJ8n2nsrC1tdU4Ns//n/+rr7Av0nwl1fMvWrSIVq1asWbNGszNzdm1axcnT56Ut/18spYkSWP7Je3X6NGjGT16NAkJCXzyySds376dTz75pNB5838J5/9vYGBAjRo1gLynmmbOnEm7du0wMTHBycmp2H2aP38+sbGxeHl5yV2n2Nvb8/XXXxe7HORdVf/000/s3LkTBwcHUlJScHV1le95tW7dms2bN6NSqQgMDOSTTz7h3Llz5focvqiq9YT9/LlOS0vj2bNn1K5dm5CQELZv386uXbto2rQpSqVS4xgBuLu707x5c95//32+/fZbOYHZ2dmxbNmyQr/LateuTVhYmPw6IyND4/5LWXl5eTF69Gitd59T9a6hyig1NZUzZ84wbdo0fH19ad68eYF5zpw5w6NHj5AkCQsLC/T09OQv+Fq1ahEeHl6mbWZmZrJ79268vLyYNm2afPPb398fPT09srKyOHv2LCqVis2bN2vc6K1ZsyaRkZFFfpn169eP3bt3Ex4eTlpaGmvXrsXb27vMTyrp6enRp08f1q5dS2pqKpGRkezcubPMX9wbNmwgOzubkJAQzp49K1+VpKWlYWVlhZGRETdu3JB7sIW8X8N37txBrVZjbm6Ovr4+SqWS+Ph4Tp06RXp6OoaGhpiamsq/yoYPH87WrVvlm8ApKSlytVtJvL292bNnD7GxsSQnJ7Nt2za5rHbt2ri7u7N8+XJSU1PlnpT79OlDWFgYu3bt4saNG3h4eHDr1q1C15+WloaZmRlmZmaEhYWxb98+uczDw4M7d+5w6tQpcnJyCAwMlB9UKGm/bty4wfXr11GpVJiYmGBoaFhstcaRI0e4f/8+GRkZrFu3jt69e8vVJ05OTiiVSpYvX16qc2xlZcXw4cM5deoUkyZNIicnh27dupGens6uXbvIzs4mNTWV69evF3o8DA0NqVGjBhkZGXz55ZdyWXZ2NkeOHCElJQUDAwPMzMzkfSruc1gaVbEn7HPnzhESEkJ2djbr1q2jTZs22Nvbk5aWhp6eHjY2NuTk5BAQEEBqamqB5T/44AP69evH+++/L+/H3/72N7766iv5B2ZiYiKnTp0CoHfv3pw9e1be5vr168v8AMTzXFxcCnSfpA3VNrHkP8nl4eHBli1bGDNmTJGPGj969Eh+cmPYsGH87W9/o0OHDkDeEzWbN2/GxcVFfnqlJA8ePMDd3Z0333yT2NhY2rdvz/nz5xk0aBBLlixh4cKFzJs3j65du2JiYqJx6Zr/xezm5lag7yuAwYMH4+vry6hRo/Dy8sLQ0JD58+eX9fAAeb9KTUxM6NGjByNGjKBfv34MHjy4VMvOmTOHWrVqYWlpSZcuXZg+fTqLFi2icePGQN4vx/Xr1+Pk5MTGjRs12kLEx8czZcoU2rVrh4+PD+3bt8fPz4/c3Fx27dpFly5daN++PZcvX2bRokUA9OzZk3HjxjFt2jScnZ3p168f58+fL1Ws77zzDu7u7vj6+jJgwAA8PDzQ19eXv3RXrlyJSqXCx8eHIUOGkJGRQYcOHRg3bhxt2rTBycmJhQsXFtnif9asWRw9ehRnZ2fmz5+Pj4+PXGZjY8O6detYtWoVbm5u3L9/H0dHRwwMDErcr7S0NObNm0f79u3p3r071tbW8pVQYfz8/Jg9ezbu7u5kZ2czd+7cAuV3797Fz6/kbs+nT59Or169+Oabb7C1teXDDz/EwMCAN954gzNnzuDu7k7v3r25ePFigWUHDBhA3bp16dKlC3379qVtW82u6w8fPoynpyfOzs7s37+fVatWAcV/DkujuJ6wyyr/+yP/b+LEiWVeB+Qlt40bN+Lm5sYff/wh72v+E4i9e/fG09MTIyOjIr/AJ06ciJeXF2PGjCEpKYnRo0fj6emJv78/Tk5OvPPOO9y4cQOApk2bsmDBAqZPn06XLl2wtLSsmn2m6e65geqrd+/e5Sqrau7du1fkn6ura7FPjVVlZ8+elbp161Zo2YABA+T/X5zHz8/vpbetVqsld3d36cKFC0XOU9xxd3d3L/e2Dx06pPFUUlH69OlTYNry5cul0aNHF1pWVdy8eVPy8fHRmObt7S0/dSaUX/fu3aU7d+5obX2vzD2WymRtbc3Ro0fp27evfCkvSRLBwcHVopFZvn79+lGvXj2Net98qampRT4RV9VkZmZy8eJF3N3dSUhIYOPGjfTo0aPQeaUX6rifV94qhZ9//pk2bdpgbGws96Lw4q/45xV33MtbX56RkcHevXsZMWJEifPWr1+fy5cv4+rqKk+bNWsWX375pUY1YlVT2p6wBd0TiaUcli9fzsKFC1myZIl8Mzw2NpYWLVqwfLnux80orXr16rF3795Cb+iXpYpC1yRJYv369XzyyScYGxvTrVs3pk6dWui89erVIzU1FXNzc5YuXSpPj4mJwcTEpFzbv3btGtOnTyc7O5smTZqwcePGYpNyccfdw8OjzNv/+eefmTx5Mh07dpTvPxRn5cqVhd7byL9PWVWVtidsQfcUUmE/m4RSSUxMlOt37e3tq90bfMWKFfTs2RNnZ+cCZUuXLmXevHk6iEo30tPTycjIqJR+08RxL7+wsDBmz55NcnKy3BO26LS0/JYuXcoPP/xAfHw8NWrUwNraWqONV3m9FoklMzOT0NDQQgcOEgTh5T0/INTLVqGKz2vVUd7z+lpUhYWGhophTgWhEgQGBpY4SFdJxOe16inreX0tEkt+i20xnKkgVIz84W2f7x2hvMTnteoo73l9LRJL/uW0GM5UECqWNqquxOe16inrea22DSQFQRCEqkkkFqHCPE6K5IvzAdyIKbyrFEEQXk0isQgVZsd/93M1+g82XtyNOvflhxQQhOdlq17uPfWyywtFey3usQiV72nGM27F3adRjfo8fBrOnfgHtKrdVNdhyVQqFRERERo9UQslMzY2xsHBQe4LTZcMDfTo/9nhci8fvKbkPtWE8hGJRagQN2PzBuEa2XogS8+t5058WJVKLBEREVhYWNCwYUOdDTZV3UiSREJCAhERETRq1EjX4QhVmEgsQoW4Ex+GiYExjnWaU8/CjjsJD3QdkobMzEyRVMogKyuLZcuWceHCBSRJokOHDvz973+XB96Ki4sD8hJ2/pNc2hqUS6h+xD0WoUI8SoqkobUDSoWSBjUcCH8WpeuQChBJpfRWrVqFkZERJ0+eZP369XJfbPkDb+UPtrV27Vp5mfyykydPMmLECBYsWKCT2IXKJxKLoHW5Ui6PnkXSwCrvl6uDpR3xaYlk5WSXsKRQFaWlpREUFMTUqVM1Bsh7ceAtgHv37pGYmFglB+USKo+oChO07klqPFk5WTSskZdY6lnaISERlRJLoxr1dRxd1XXq1CnWrFmDkZERDx484NKlS2Xqn+nixYuoVCo6d+6s1bjCw8OxtrYmICBAHvhrzpw5GBsbawy8BXk9EEdHRyNJUpGDcj3fWWtycjLJycka2yvLENxC1SQSi6B1Ecl5XwwOlnnjZNS1yOsePjrliUgsxdi/fz9TpkzRGI3zRTk5OUUOU33p0iXS09O1nljUajXh4eG0atWKWbNmcfjwYSZPnsy6deteet27d+8mICBAC1EKVYlILILWxabm3ci1s6gNgK1ZXlf08ekJOoupqlu2bBlXrlzh4cOH7N27l0uXLvHf//4XMzMzPD098fHx4ffff6dZs2aMGzeOzz//nIyMDHJzcxk4cCCdO3dm//795Obm8ttvv9G3b1/Gjx+vldjs7e3R19eXq7WaNWtGjRo1MDY2lgfeypeQkIC9vT2SJJVqUK733nuvwBDd+f1TCdWXSCyC1sWkxmFqYIKFoRkApgYmmBmYEJdWdevXT4c85sdLjytk3T3bv4GnyxvFzjNnzhxu3bqFv78/3bt3p3nz5hrlqampHDx4EMgbQ8PT05MPP/wQgGfPnmFlZcXw4cNJT09n1qxZWo3fxsYGNzc3fv31Vzp37kxkZCQJCQk0bNhQHnirXbt2ADRp0kSu6irNoFyWlpbVatRVoXREYhG0LiY1DjtzW42nrmzNahKXJq5YymvAgAHy/66urqxatYqMjAzc3NwqZbTPxYsXM2fOHFasWEFOTg4rV67E0tKSRYsWMXv2bLla7NNPP5WXyS/btGmTPCiX8HoQiUXQutjUOBrV0PyFXsusplxFVhV5upR8VaFLpqam8v+9e/embdu2/Prrr2zbto3//Oc/rF69ukK3X79+fb799lsAbt26RcuWLQFo3LgxBw4cICIiAi8vL+rX/+seWn6Z8PoRjxsLWqXOVROXloCdueb4DbamNsSnJfIaDFha4R49eoStrS2DBg1i4sSJ3Lx5EwBzc3NSUlJ0HJ0giCsWQcvi0xNRS7nUeSGx1DK1ISMnk3RVBmaGpkUsLZTG8ePHCQ4OxsDAAIVCwZw5cwDo0aMHQUFB+Pn5afXmvSCUlUgsglbF5D8R9kJiqWFiBcDTzGcisRQhv6oJ4M6dO/L/p0+f1phvwoQJTJgwocDy9evX5/Dh8nfKKAjaIqrCBK3661FjzcRik59YMp5VekyCIFQukVgErYpJicNQz4AaxlYa061FYhGE14ZILIJWxaTGUeeFR40BOdGIxCIIrz6RWAStiv3/xPIiEwNjTPSNeZopEosgvOpEYhG0JlfKJSYtHjuzWoWWW5tYiisWQXgNiMQiaM3TjGeo1Cq5j7AX2ZhY8zQjqZKjEgShsonEImhNUY8a57M2tuRpZnKhZULVsmHDBtEFi1BuIrEIWhOT8gSgyCuWGv9/xSJa3wvCq63SGkiWZvxrtVrN0qVL+fnnn1EoFIwfP56hQ4dqzPPgwQMGDhzIiBEjtN6Lq/ByYtPi0VPqUcukRqHlNYytyFarROv7QmRkZDBr1izu37+Pvr4+jRo1Yt26dRw6dIi9e/eiVqsxNzdn0aJFvPnmmwB8/fXXHD16FIVCgampKXv37kWpVLJ161aOHDkCwNtvv828efMwMzNjw4YNPHz4kJSUFMLDw3njjTdYt24dJiYmpKSkMHfuXO7evYutrS12dnbUqlX4vTJBKEmlJZb88a/9/Pw4fPgwCxYskMfJzhccHMzjx4/54YcfSEpKYsCAAXTs2BEHh7yRCNVqNQsXLqRHjx6VFbZQBjEpcdQxq4VSWfiFcFVufZ9y4ywp10+XPGM5WLTxxKJ1t2Ln+eWXX0hLS+PYsWNAXlf4ISEhHD9+nMDAQAwNDTl37hxz5sxh//79HDp0iNOnT7Nv3z7Mzc15+vQpSqWSc+fOceTIEfbv34+ZmRmzZs1i06ZNzJgxA4DQ0FAOHjyIhYUFY8eOJTg4mHfeeYeNGzdiZmbGiRMnSExMZNCgQcUOOCYIxSl1VdipU6fIyckp10ZKO/71sWPHGDp0KEqlEhsbG3r06MGJEyfk8q1bt9KtW7cCVzpC1RCT+qTI+yvwV2JJEk+GFdCiRQvCwsJYvHgxx48fx9DQkNOnT3P79m2GDh2Kn58fa9askYftPXPmDH/7298wNzcHoEaNvKvECxcu4OPjg7m5OQqFgnfeeYcLFy7I2+ncuTOWlpYoFApat27N48d5Y9BcvHiRIUOGAHnjr/Ts2bMyd194xZT6imX9+vXMmzcPHx8f/Pz8aNOmTak3Eh0dXarxr6Ojo6lbt6782t7eXv4g3b59m19++YU9e/awadOmIrclxtDWDUmSiEmNo5Vt0yLnsTbOG9DpaUbVu4Fv0bpbiVcVFal+/focPXqU33//nfPnz7N27Vq8vLwYPHgwU6dO1dp2jIyM5P/19PTIysrS2roFIV+pr1iOHDnCrl27MDIyYvLkyfTu3ZtNmzYRERFRkfEBoFKpmD9/PosXL5aTU1F2796Nl5eXxp8Y5rTiPctKITMnq8gb9/Bc63vRSLKAmJgY9PT06NGjB59//jmJiYl4enpy+PBh+YeRWq0mNDQUgO7du7Nv3z5SU1MBePr0KQAdO3bk+PHjpKamIkkSBw8epFOnTiVuv0OHDnz33Xfyuk6dOlURuym8Jsp0j6VFixa0aNGCmTNncuHCBZYvX86GDRtwdnZm2LBh9OvXr9D6dXt7+1KNf21vb09UVBStW7cG/rqCiYuL4/Hjx3I34MnJyUiSRGpqKn//+9811iHG0NaNqOS8Lz8786ITi4mBMYZ6BqIqrBB37txhzZo1AOTm5jJ+/HhcXV355JNP+Oijj1Cr1ahUKvr06YOjoyMDBgwgNjaWYcOGoa+vj6mpKYGBgXh4eHDnzh2GDx8OgKOjIx999FGJ2//444+ZM2cOffr0wdbWFhcXlwrdX+HVVuab948fP+bIkSMcOXIEhULBlClTsLe3JzAwkB9++IGAgIACy9SsWbNU41/36dOHAwcO0KtXL5KSkjh16hSBgYHUrVuXixcvyvNt2LChyLG9xRjauvH4WRQAb1jXLXIehUJBDWMrccVSCA8PDzw8PApM9/X1xdfXt8B0hUJRZPf548ePL3QslsmTJxf52sLCgg0bNpQndEEooNSJJTAwkMOHD/Po0SO8vb1ZuXIlbdu2lct79+5d7CV3UeNff/DBB0yZMoW3334bPz8/rl+/Tq9evQCYOHGixlCnQtUV/iwKMwOTAr0av8jaxIok0UhSEF5ppU4s58+fZ8yYMXh5eWFoaFig3MTEpNhfPEWNf71t2zb5fz09PRYvXlxiLC/+8hJ0L/xZFPWt6sq9GkvqHDIf/w9JkjBp0AqFngGQd5/l8bNIXYYqCEIFK/XN+/bt2+Pt7V0gqezcuVP+v3PnztqLTKg2JEmSEwuAKukJEds/I3rvYmL2LSFi6zRUiXlVZdYmlqIqrBoLCAhgwIAB3L17F4Br167h6+vL6NGjgb8eIni+rHfv3vj7+5OQkKCTmIXKV+rEsnHjxkKnb968WWvBCNVTQvpT0lQZ1LeqizozjejAhahTk6g94FNqD56OOjOV6L1LUGekUsPYigxVJlk52boOWyijP/74g2vXrmFrm9dWKTc3lxkzZmg0dt6+fXuBspMnT+Li4sLq1at1FrtQuUqsCstvXKVWq/n99981+nmKiIjAzMys4qITqoV7iQ8BaFqzEQk/7CDnWTx1Ry/F2KE5APoWtYjaM5eEH7+hxtuuQN4jx8U1phSqluzsbJYsWcKaNWvkJ85CQ0MxMjLCxcVFbnZw9uzZAmUAw4cPx8vLiy+++EIn8QuVq8TEMnfuXCDvjTVnzhx5ukKhwNbWlnnz5lVcdEK1cC/+IQZ6BtRJSeHJzXNYuw+RkwqAcb2mWHfwI+m37zBr/BaQ1/peJJbqY926dfj6+srdK0HBBs2QVy2alJRUoMzGxobc3Fy5r8B8okHzq6nExHL6dF7/STNnzmTlypUVHpBQ/dxNeMibNd4g+fy/0DOzxrrTwALzWHccQPK1UyhungeFaCRZ2W7fvs2yZcu4ffs26enpQF4SUCgUcqPLoly9epXQ0FCmT5+u9bh2795daBMFoXor9VNhIqkIhUnNTuN+4p/0retE5uML1AWD57gAACAASURBVOw1FqWhcYH5lMZmWLn2Jfnn/fCmrRhJshAbNmzgww8/xNDQkNmzZ+Po6MioUaMKzLdu3TqaNm2Kj49Pqdbr6emJUqmkX79+zJs3D2PjguenOJcvXyYsLAwvLy8gr++/sWPH8u677xIVFaUxr0KhwNraWm7snC8xMRGlUqlxtQKiQfOrqtjE4u3tzfHjx4G8Blz5j5K+KL9eVXj93Ii5Ra6US5PoKJQmFli09SpyXkunnpj9chAliLYshQgICMDf37/Qx/mfV1TfYbm5uSgUikI/p0+fPmXq1KlFfoaL82KDy86dO7Njxw6aNGnCv//9b0JCQrCzswOQG3k6OjqSmZlJSEgILi4u7N+/nz59+hRYt2jQ/GoqNrE8313KqlWrKjwYofq5EP5fLAxMsb19E4sOvigNjIqcV8/MCkvHLlg8u8HTtMQi53sd5bffGj58OEqlknr16nH37l1Gjx5NTEwMbdu2ZcWKFSgUCo2rmQ0bNnDv3j1SU1OJioriX//6F/fu3ZPX5+rqiiRJeHp6EhwcXGgr/vJSKpWsXLmShQsXkpaWBsC4ceMKlGVlZVGvXj3xHfIaKTaxPN9fUPv27Ss8GKF6ScpMJiTyOt2M7VDyGEvn3hrlkiRx7r8RHPvtTzKycujcpi59Hbthce4qCQnhOoq6cOce/s6Zh79VyLq7N+qER6MOxc6zcOFC9u7dK4+jMnv2bO7du8euXbtQKBQMHDiQ3377DXd39wLL3rhxg++++w4bGxuys7P59NNPWb16NW5ubhw7dozAwECGDBnCnDlz+Prrr6lZs6bG8i+Oi1SSbdu20axZMwCcnZ0JDg4mIiICLy8vjW6a8suE10+p77Hs3LmTDh060LJlS65du8Ynn3yCUqlkzZo1ODk5VWSMQhV15NYP5EoSzo/+xLSJMwbWmh1Q7jz6Pw6dvU8DOwsszAz554nbhPzPGssaBiSmxuso6uqjR48ecjf3rVq14vHjx4Umlq5du8pf6A8ePMDExAQ3NzcAfHx8WLBgAcuXL8fBwYGePXtqdJ0vCBWh1Ill165d8kBAa9as4f3338fMzIxly5YV2lWL8Gp7kPiY4/fP0tm2OTXvn8e8h2YHimeuhHPo7H18OjXkw4GtUSoV/HI9klX/vEJLC0ue6SWR8ywOfauq8cixR6MOJV5VVLYXx05Rq9WFzldSWzKFQsHDhw+5dOlSifdvBEEbSt3yPiUlBQsLC1JTU7lz5w7vvvsuQ4cO5eHDhxUZn1AFpWdnsPbCdqyNLOmXrkRhaIxpk3Zy+dOUTLZ8d4NWjWwY//9JBaBzm3pMGNSax8+sSNNXknTzrI72oGoyMzOTx1cprzfffFO+aQ5w4sQJkpOTadWqFWFhYdoIUxBKVOorFnt7e/773/9y//59XFxc0NPTIzU1tcSBt4RXiyRJbL2yl7i0BBZ6TEERuAzTZu01btrvO3mHrGw1U4Y5oafUfAqpT4cG/HjfgQjCCb95jpruQ8r1pNKryN/fn9GjR2NsbEy9evXKtQ5DQ0O+/PJLjZv3devWpU6dOvj7+9OzZ88C91i0OUKlIEAZEsvMmTOZMmUKhoaGrF+/Hsgbd/vtt9+usOCEqufnR5f47XEIw9/2pUFKKjEZqZi1+qvePyo+lZMXH+HTsSH1bM0LLK9QKPDt2IpNVy6QnBZH9pM/MarTqDJ3ocqaNGkSkyZNKrRs+fLlhf5fWE/fLi4uGjfNFyxYwOeff063bt1QqVSiZbtQ4UqdWDw8PP6vvTuPi6reHz/+mp1hGfYdBcUFFNEUNTW75galiWVeTStbzBZv3Z+VRd6ukppFWl5L7bZ4zZKbfm3BK5KalqWWC+4IiiGoyL7JOgyz/P4gJhGEUQdQ+Dwfj1E46/vMwnvOOZ/P583evXvrTYuIiGi0bbrQPmn11aw//i3dXQKYGBROwZYPkdrYYdu1r3mZ+L3pSCXw19E9rrkdP2c3AC7LZJz9eTshf21YrEqwLjFGl9CarquCZFlZGenp6eY263WGDBli1aCEW9O2s7sp0Zby8rBZYKihIvUg9sHDzLVWKrU17Dx4gbv6+eKsuXbvbmd1bTGwDIUrnc7+hq7mKZQKRascQ0d18eK1m3eLYnqCtVmcWL799lsWLlyIra1tvSEhJBIJu3btapHghFuHwWhg++8/08ezJz3dAik//RsmnRa73n9eBvsp8SJV1Xruv6trk9tytNEgQYKkUxccL+/jx627iZg4pqUPoUMbM2YMEomk3ujkdfe2UlJS2iosoZ2yOLEsX76cFStWNFqXW2j/DmedpLCymCf7TwGg4tQ+ZHZOqP1DADAaTWzZm06Pzk706Ozc5LbkUhlOag0yd2f0yCk8tpviUcNxdri+MaxuVt0gjB3B6dOn6/2en5/PypUr63WCtsSViUkQrsXi5sYGg0FUiOzA9l44hKPKgf7eIRirq6j8/TB2wUOQSGtbBR47m8+l/HLGN3O2UsfD1pV8bQnKwAH0kaUTm3CqJcNvwMbGhsLCwg77h9Ld3Z1//OMfvP/++xavYzKZKCwsvO5BLIWOx+IzlqeffpqPPvqI559/HqnU4nwktANafTVHsk4yossQZFIZZakHMel12Pf684tG/N5zODmouKuvTxNb+pO7nSuphedw7x+JMe0AmUf3kzYskEA/p+ZXtgI/Pz8yMzPJz89vlf3dijIyMigvL7+uS2E2Njb1arIIQmOuq+d9QUEBn332WYOhr8Xoxu3bkawkdIYahnaq7QRZkbwPmcYNlV9ty6/sggoSU3KZMronCrll/Zrc7Vz57eJhVF36IFHZMdhwnk83J/H288Na5fKUQqGgS5eO08x52rRp9Z7Xqqoqfv/9d55//nmCg4PbMDKhPbI4sYiRSTuuxKwTaFT2BLl1w1BVRuW5YzgOGodEUnvmunVfOlKJhIgh/hZv093OFYPJSImuEvteQ+lzfDfrz+Xw64lshll41iNYbvLkyfV+V6vVBAUFERAQ0DYBCe2axYnlZkc3Tk9PJyoqylyaNCYmpsGb2mAwsHjxYvbs2YNEImHWrFnmD8SqVatISEhAKpWiUCiYM2cOw4cPv6mYhOYZTUZO5p4m1KsXUqmU0tMHwGjAvlftc19VrWfnwfMM6+uDq6Pa4u162NX2/s6rKKRL7+GUHf2BezwKWbMliTt6umNrI5ofW9O4ceP47rvvSElJMVeQrKsOK4r4CdZm8c0SnU7H8uXLGTVqFAMG1F4S2bt3L+vXr7do/QULFjBt2jS2b9/OtGnTmD9/foNltmzZwoULF9ixYwcbN27kww8/JDMzE4DQ0FC+/vprtmzZwpIlS5gzZw5ardbS8IUbdKEki8vaUkI9gwCoSN6LwsUbpVftZaSfDl+kQtt8E+Oruf+RWPIrCrHpHIzMwZUIzxwKS6r4JO6kdQ9CICoqinXr1mFnZ0fnzp3rPQTB2iw+Y1myZAm5ubksW7aMp59+GoDu3bvz9ttvN1o+9UqFhYUkJyezdu1aAMaPH8+iRYsoKiqqV78hISGByZMnI5VKcXFxYfTo0Wzbto2ZM2fWOzvp2bMnJpOJkpISc+U6oWWcyE0GINQrGH15MVXnT+E07EFzn4j4vefo5udIT/+mmxhfzc22dvn8ykIkEin2ve/i8sF4Hv7LeGJ3X6R/Tw/uvkPcJLaWPXv2sGvXLlGtUWgVFieWnTt3smPHDmxtbc2twjw9PcnNzW123ezsbDw9Pc0DVspkMjw8PMjOzq6XWLKzs/Hx+fP6ure3d6PjGsXFxdG5c+dGk0ppaSmlpfXL3oqxkW7ciZzTdHL0wUXtxOVDCWAymluDHT2Tz8Xccl6a1v+6b7grZAqc1Y7k/lGXxb73cC7v30y4dz5HAlxYseEoHi62BPm7NLMlwRLe3t7odLq2DkPoICxOLAqFokE9iKKiogYtxFrawYMHWbFiBf/5z38anb9u3TpWrlzZqjG1Vzq9jpT8s4ztVtsptjx5L0qPzijda4cA2bwnDWcHFXf1vbGReH0dvMgqrU36Ss8AFG5+VCXv5R9P/JNXPviFNz/dz4Kn7xTJxQomTpzI888/z2OPPdZgdGMxJJNgbRYnloiICF577TVef/11APLy8liyZAnjxo1rdl1vb29yc3MxGAzmgkV5eXl4e3s3WC4rK4vQ0FCg4RnM0aNHmTt3LqtXr6Zr18av6c+YMYMHHnig3rScnBymT59u6aEKf0gp+J0ao56+XsHUlORSnXkG5xG1z+PF3DKOnM7jkYggFPIb69fkp/Hm54z95h7w9r2HU/zzV3gYSln0zFDmf/wb/1i9j8fH9+a+oQHIZKL/1I2quxd6dYdIMSST0BIsTixz5szhvffeY8KECVRVVREeHs5DDz3E7Nmzm13X1dWV4OBg4uPjiYyMJD4+nuDg4HqXwaA2eW3atImxY8dSUlLCzp07iY2NBWrres+ZM4cPPviA3r17X3NfGo1GXEe2khM5KcilcoLdu1N+IB4A+961l8G27DmHQi4lYkjADW/fz9GLKr2WoqoSXG2dse99F8U/f0VZ0i94DZvEuy8MZ8XGo3wSd5Ite89x39AuDO3jjYeLrTUOr0OpawEmCK3B4sRy4cIFunTpwjPPPIPBYGD06NH07NnT4h1FR0cTFRXF6tWr0Wg0xMTEALU9+l988UX69OlDZGQkx48fZ+zYsQDMnj3bPPLqm2++iVarrdea7N13372uGITrcyInhSC3QFRyJXmn9qDyC0Lh5EFxmZZdiRcZ0d8PR/sbr5/uq6k9Y80szcbV1hmFsxc2AX0oPbwNpzsn4OSgYv5Tg9mflM2mXWdZ878k1vwviQBvDQOCPBjYy4sgf2dxJtPCiouLefXVV7lw4QJKpRJ/f38WLlyIi4sLx44dY/78+ebKl8XFxeae+XXzqqur8fX1ZenSpQ0uwwntU7OJxWQyMW/ePOLi4vDy8sLDw4Pc3FxWrVpFZGQkS5YssejGbWBgIJs2bWow/dNPPzX/LJPJzJXvrvbNN980uw/BekqqLnP+8iWmhU6kOjeDmvwLuIbXtgbc/HMaer2BSSO739Q+/DS1jS8ulebQ16sXAE6D7ydn4xLKk3/Foc9fkEgkDOnjw5A+PmQVlLP/ZA6HT+cS93Ma3/z0O+7OaiaP7M6Ywf7IRYJpERKJhJkzZzJ48GAAYmJiWLZsGYsXL2bu3Lm8/fbbeHl5MWrUKD777DNWrFiB0Wg0zwsLC2P16tUsW7ZM1IXpIJr9JG7cuJGDBw+yceNGfvrpJzZu3Mju3bvZsGEDiYmJbNiwoTXiFFrZidza0XBDPYMoP7UHJFLsg4dQWqFj67507urn22iFyOuhUTngqHLgXPEF8zR14B0o3Py4fGBLgwEifdzsefCebrz13DBiF97Lq4+G4aKxYfU3J5j7wS+czy69eheCFTg5OZmTCkC/fv3IysoiKSkJlUpVb4TkuuGdrp43depUtm3b1qpxC22n2cSyefNm3njjDfMN9TqhoaHMmzePzZs3t1hwQts5kZuCg9IOfydfKk7tRd21HzI7R/73SxpanaHJCpGWkkgkdHftwtmC9CumSXEcPAFdbjqVZxOvua6dWsHwfr4sfWE4UY8NJL+kipdW/MK+41k3HZdwbUajka+++oqRI0c2aFwDmPuXXT3PxcUFo9FISUlJveVLS0vJzMys9xDdA25/zSaWtLQ0Bg4c2Oi8gQMHkpaWZvWghLZlMpk4mXOaEM8gdJln0JcWYB8ynPKqGrbsPcfQUG/8vazTQKKHW1eyy/MorS43T3MIHYHCxYei3bGYjIYm1q5NTsP6+vDhK/fQ1UfDO18cYtOuVKvEJjS0aNEibG1tm+0Ubal169YxatSoeg/RgvP212xiMRgM2Ns3fsnD3t4eo9Fo9aCEtpVZmk2x9jKhnkGUHfsRiVKNXY9BfLf7dyq1eqaMtl6DiR6utUPDnC284qxFKsN5xMPU5F+k7MRPFm3H2cGGt54bxt13+PJFQgpfJCR32ForLSUmJobz58/zr3/9C6lUau4ecCWJRIKTk1ODeUVFRUil0gb93mbMmMGuXbvqPepaggq3r2Zv3uv1evbv33/ND+nVnSaF29+JnNr6HL2d/alIWYV9nxGUaCHu5zTu7udLV19Hq+0r0CUAuVROUu4ZBvj0MU+3CxqCTadgin78ErvuA5HZNb9PpULGS9MGoFbJ2bTrLFqdgZkTQpBKO0aVyJb0/vvvk5SUxCeffIJSqQQgJCQErVZLYmKieRSMugqzV84LCwtjw4YNRERENNiu6B7QPjWbWFxdXZk3b94151/dF0W4/R3JTsLXwQv1uVNU6nVo7hjNmh1nMBiMPHKvdWt3qORKQjx6kJh1gsf6TTK3MJRIJLjd9yyZn71MwfbP8HjgJYtaH8qkEmY/1BcbpZzNv6ShrdYz+6G+oknyTTh79iwff/wxAQEBTJ06FagtlLZq1SreffddFixYQEVFBQAzZ84EQCqVmudd2dxY6BiaTSyiY1XHUq6rIDkvlfE9R1N2aCdKzy4UyD3ZfuAU9w0JwNvNzur7DPPty2eHv+J8SSYBzp3M05VufjgPn0Lx7lhKO/fGMazhN97GSCQSnprQG7VKzoYfzlBWqWPuI2EoFZYVIRPq6969O2fOnGl0Xv/+/dmyZQuZmZmMGjWq3hfNunlCxyO+xgn1HM06hcFkpJ/CCV1uOpo7xvBlQgpKuZS/jrn5lmCNGdppAAqZgh2//9JgntPQidh2G0DhD2upSj9h8TYlEgnTI4J4emII+5NyiP50P5XaGmuGLXRwupqbvw1gjW3ciizueS90DPszj+Bs44hz0n50ageynULZd+IgD4/tibODTYvs015lx3D/QfycsZ+JweF42LuZ50kkUtwnvEjWl/8kZ9M7eE2Zh9o/xOJtTxgeiMZOxb++OsLrq/cR/fSdLXYcQseiVMi4/+Wb626x5b1IK0VzaxFnLIJZqbaMI1knGeIZjPbsYTT9x7J2WxqO9kom/iWwRfc9ufc4pBIp/z60Hv1VTYxlanu8py1ArnEj+7+LKD36w3W1+BrR349/PjWYS/nlvPrhHrLyy5tfSRCEGyYSi2D2y/mDGExG7sjJQyJXkGJzB6fOFfJIRHCLlwp2tXXmqQFTSco7w4f716LT168dIrd3wmfGEtQBIRQk/JvcTe9QcznP4u0PCPJkyXPDqNTqmfvhHs6cL7L2IQiC8AeRWAQA9AY9W1N30cPRD4dTB7DrH8GanRfp4qNhzGD/VolhRJchPNL3QfZfPMI/dy0jp6x+4pCp7fGaMg+XUTOoyjjJxY9eIP/7j6kpsSzB9OjszNIXh2Nno2DeR79y8JTo4S0ILUEkFgGAH9N/pbCymJFF5UhVanbXhJJfXMWsiX2QtWI/kAlBY3ht+HPkVxbx2o632XfhUL35EqkMpzsn0OmZFTj0HUnZsR+5uHo2uXHLqc451+z2fdzsefeF4XT2cuCttQf4/reMljkQQejARGJpxwxGAxnFmWQUZ1JjuHaLqPyKQv57Io4eajf801NR3DmFjb9c4q6+PoQEul1zvZbS36cP746dR2dHH1b89h8+ORTb8NKYozvu9z5D59mrcRw8nsqzh7m0Zi7ZsdFUnjvW5D0YJwcVS54bRv8gT1Z/fZz121JEL31BsCLRKqwdMplM7Dq3j69ObqbsjzG4FDIFvdy7cYd3CP29Q/By8AAgo/giy3/9DIxGIlPTUPuH8J8zbkABT4y/dkG1luZm58KCkS/xf0lbiEvZzpnCc7w2/Hk87OrX85BrXHEdNQPnYQ9RevQHLh+MJ+erRSg9AnC5Zxq23QY0un21Ss4bTwxi1dfH2fhDKoUlWmZP7iuG3hcEKxCJpR1af2A9W87/StdqI+OKy5BIZGQ6OZGan8HnOSl8fnQTLmonpBIpBZVFaORqHs8uwdPGkYweD3Ng4xmeGN+7zSs1yqUypoVOpJd7d1b8toYFu97jnyNexOePOi5XktrY4TRkIo4Dx1F+ag8lv35HzsYl2PYcjNvYp5BrGhaYksmkvPDXfrg5qflqxxmKyrREPTYQtUp8LAThZoivZ+2IyWjgp+0r2XL+VwaV6fh/mmDuCXuIv/QJZ5LUhTlnM5mbUUhkiY7AagNdakxE6m35e+oFApWOaB56g4+2ZtDNz5HIu7u29eGY9fPuTfTIl9Ab9cz/8T0yii9ec1mJXIFD35H4zXof5xHTqUo7SuYn/4/yU3sbX14iYVp4EH+b3I9jZ/KY99E+SsqqW+pQBKFDEF/N2gljTTVp37zDl4YsfBVqXpj6Fir7+uO4Gasr8Th3DP/UQ2gvpmCsrkTu6IH9X6bjEHYvMf89QVmljjdnDbnlxtbyd/LjzVEvs2j3CqJ/Wk7U8OcJcu92zeUlMgXOwx7EvtdQ8jZ/QF7ccip/P4xr+ExkNg2HpQm/0x9nBxUxXyby6so9LJw1BC9X6w9fIwgdwa3110O4IUZtBdlfLSS24jxahZw5Y+c2SCoAUpUt9sFD8Yj8O53/9m8CXv4Cv5nLcBr6ANsOZvHbyWxmjOtl1dGLrcnHwZNFI1/ByUbD4p8/4EjWyWbXUTh74fPYIpzvnkr5qb1c+vQlqjIaX29Qby/eenYo5ZU65n64h7TMkkaXEwShaSKx3OYMVWVkxUaz7/IFku1VTA2dSGcn3+vaxrHUPD77XxJhwZ5E3t2yPexvlpudCwtHvoyfxpt39/6bXzIONLuORCrDefhkfGa8hUSuIDs2moLtn2HUaRssGxTgQszfhqOQS4latZfdRzJb4jAEoV0TieU2ZqgsIzv2TXKKM4n3cibYvTvje4y6rm2cPl/Eks8P4efhwMvTB9wWtUs0Ng4suGcOvdy7s/LA52xKisdoar7gnI1vD3xnvodm0HhKE7eR+elLVKYdbbBcJ08Hlr14N4F+TrwXe5jVXx9HW61viUMRhJtyqw6EKe6x3KYMFZfJ/u+baAuziOsTjERbwuzBM5BKLf+usO9EFu//9wiuGhuin74Te3XLDttiTWqFDVF3z+aTxFg2ndpKWtF5/nbn49grm74vIlWocBvzBHY9B5Mfv4qcDYtRd+2Ly4hHUHn/2WDBRWPDW88O5cvvU/jmp99JPJ3Lsw+GMjDY06K6MILQGm7VgTDFGcttSJd/gUtro6gpymb/sNGklufw1ICpDfp4XEtWQTnL1h/mnXWH8Pdy4N0XhuPqqG7hqK1PKVMwe9AMnuo/leO5Kbz8/SIOZh6zaF115150euZfuI55guqsNC79Zy5ZX86nPOU3jDW1rcJkMimPj+/NO7PvwkYpZ9GaA0St2sux1DzRoVIQmtBqZyzp6elERUVRUlKCk5MTMTExBAQE1FvGYDCwePFi9uzZg0QiYdasWUyePLnZeR2FyWSi7PiPFP6wFqlCxbnwh/nfme8ZETCEuwMGN1heV2Og4HIVhSVa8kuqyC2q5PjZfJLTC5FJpUwd05O/ju6BQn77fr+QSCSEd/8L3V278NGhL1m272P6efXiryH30801oOl1ZQocB43HIfQeSo/tojQxgbxvlyFR2GDbrT/qrn1Rd+5Nry5erHhpBDv2Z/B/u87yz49/w9fdjlEDO3NniDd+HvbiLEYQrtBqiWXBggVMmzaNyMhINm/ezPz58/niiy/qLbNlyxYuXLjAjh07KCkpYeLEiQwZMgQ/P78m57V3JpMJbcZJivf8H9qLKSg79yKxzwC+St1Gb4+ejA+YwKHkHDLzyrmUX177f145JeUN+2N09XFkyuie3Dc0AGdN+6lL0tWlM2+PiSIhdRdxKTuYtzOGTo4+hPmE0sW5E85qR2zkKgxGAzqDHp1Bh9FkxETtmYc6sBf2QQOwy7sIZ4+hPXuQipRfAZDZO6PyDmSIZxfuesifYwUefH+ynC8SUvgiIQUPF1tCuroS6OtIFx9HPF1tcdXY3HJNtgWhtbRKYiksLCQ5OZm1a9cCMH78eBYtWkRRUVG9UqYJCQlMnjwZqVSKi4sLo0ePZtu2bcycObPJebcyo9GIyWT682E0YMKEyQgmkxGjeZ4RkwmMhhpMlaXoKy+jL8pCm5NOdcZxdFXFFNg5ktx9MEe4TPmZ7agq/Th2xJ/Z8T+b96exU+LnYc/AXp54utji5qTGzVGNm7MaV0cbbJTt97aaXCpjQtBYxgTezc8Z+9l3IZHNp3dYdGP/ShIk2Pu74CD3wc4IKl01cm0mstRUFKeNKE0muijl9Oxjhx41ZdUqCnIUpKfL0enVaGvU6Ax2ONo54GDvgMbOFo2DDRo7JY72KhztlWjsVNirFaiUMtQqee3/SjlKhQyJBHEGJNzWWuWvTHZ2Np6enshktTXHZTIZHh4eZGdn10ss2dnZ+Pj4mH/39vYmJyen2XlXKi0tpbS0tN60xpZrzCdxJ9l58AJgwhT4G6hLARMKiZ4rP+amq3+W1J9eb741/kB4yQH3PzaYjqlSg6ZiMP7qIDoNc8DPwx4/Dwd8PezR2Clvfn+3ObXChojuI4joPgKdXkdmaTal1RVo9VrkUhlKmRK5VI5cKjOvo9VXU1pdTml1GeW6Ckq15ZTqyimrLqdcV4nOzo5qfTXVNVp0hhp0JgOgB8pAXQZODeMo/OMhMZmQmkBaBpKy2t//1PD9YY27NyaghiYaY+R2g/xrdzCtI5HAMw/0YWRYZytEJXQU7e7r67p161i5cuUNrdu/pwfSPxJBpqGCCmMxSMChKge5QYtEIsFE7TdaoN6/5qmSK36u+0ly5dJX/i/BPLtuWxIJJpkSo0yFQWaLSWmHSqXATqXE08GNnh7+9PTyvS2aBd8KlHIlXV2sX0/GZDJRY6ih2qBDW6Ol6nI+FRVFlFUUUVZZQpm2nEq9FoNRj8FowGg0YDAZqTEaMRhMGI1GDMbas1Wj0YTRxJ9ntbU7uGJf1x+fXqqifSP8oQAACVhJREFUXO3deOyAV0AgTl0bn3+1QN9GsqYgNKFVEou3tze5ubkYDAZkMhkGg4G8vDy8vb0bLJeVlUVoaChQ/yylqXlXmjFjBg888EC9aTk5OUyfPr3ZOMOCPQkL9vzjN8vrqgsdj0QiQSlXopQrcVDZg33rlxe4HVjSaEdof1rl7qKrqyvBwcHEx8cDEB8fT3BwcL3LYAARERFs2rQJo9FIUVERO3fuJDw8vNl5V9JoNPj5+dV7eHk1HA1XEISWV9doZ/v27UybNo358+e3dUhCK2i1S2HR0dFERUWxevVqNBoNMTExADz99NO8+OKL9OnTh8jISI4fP87YsWMBmD17Np06dQJocl5zDIbanqWW3msRBOH61H226j5rYFmjncbuiV66dKneNptSU1l0wzFnZt78cD03s//bIYbGXldLSEwdoKdXYmKiRZfCBEG4ObGxsYSFhQGQlJTEa6+9xtatW83z77vvPpYuXUrv3rVF5D788MMbvicqtJ4rX1dLtLub940JCQkhNjYWd3d3c8u09qjuXlJsbGyHuvzXUY8bbp1jNxgM5OfnExJyffcmG7snqtPpuHjxIgEBATf8eb0Vnpe2jsEa+7/R17VDJBYbG5vryra3Oy8vrw7RcfRqHfW44dY4dn//+q3vLGm0o9Fo0Gg0DbbVtat1Cs3dCs9LW8dws/u/+nW1hOgaLAhCi7C00Y7Q/nSIMxZBENrGtRrtCO2bSCyCILSYwMBANm3a1NZhCK1MFh0dHd3WQQjWo1KpGDx4MCqVqq1DaVUd9bihYx97U26F56WtY2ir/XeI5saCIAhC6xE37wVBEASrEolFEARBsCqRWNqJ9PR0pkyZQnh4OFOmTCEjI6OtQ2oxI0eOJCIigsjISCIjI9mzZw8Ax44dY8KECYSHh/Pkk09SWFjYxpHenJiYGEaOHEnPnj1JTU01T2/qte5I7wPhFmYS2oVHH33UFBcXZzKZTKa4uDjTo48+2sYRtZx77rnHdObMmXrTDAaDafTo0aZDhw6ZTCaTadWqVaaoqKi2CM9qDh06ZMrKympwvE291h3pfdCUy5cvm3744QdTSkpKq+xPr9ebKisrG0yvrKw06fX6VonhViLOWNqBusH+xo8fD9QO9pecnExR0c0NTnc7SUpKQqVSmUdYmDp1Ktu2bWvjqG5OWFhYg9ISTb3WHfl98Morr3D69GkASkpKuP/++1m+fDlPPvlkqzR3XrZsmbkj6JXi4+N57733Wnz/dfbv388jjzzC4MGDGTx4MM8++yxnzpwBaofKaS0isbQDTVXobK9eeeUV7r//fqKjoyktLW1Qn8fFxQWj0UhJSUkbRml9Tb3WHfF9UCc5OZmgoCAANm/eTGBgIFu3buXbb79l/fr1Lb7/AwcOMGnSpAbTJ02axC+//NLi+wfYtm0br776KuPGjePzzz/n888/5+677+bvf/87p0+f5rnnnmuVOEB0kBRuQ7GxsXh7e6PT6XjrrbdYuHAhY8aMaeuwhDZ0ZT+Nw4cPM3r0aKB2nCyJNcqDN8NgMCCVNvyeLpVKW2X/AB9//DFr1qyhe/fu5mnBwcGEhYUxefJk85lsaxBnLO3AlYP9Ades0Nle1B2XUqlk2rRpHDlyxFxhtE5RURFSqRQnp/ZVVrep17qjvQ+ulpubi1ar5eDBgwwaNMg8vbq6usX3rdVqqaqqajC9oqKi1S5BVVdX10sqdXr06IGHhwcLFy5slThAJJZ2oSMN9ldZWUlZWRlQWyM+ISGB4OBgQkJC0Gq1JCYmArBhwwYiIiLaMtQW0dRr3ZHeB1ebNWsWEydOZOzYsQwYMIBu3boBtS0FGythbm333Xcfr732GuXl5eZpZWVlvPHGG632PqypqaGmpqbBdJ1Oh8lkatWSIaLnfTuRlpZGVFQUpaWl5sH+rDX0+K3k4sWLvPDCCxgMBoxGI4GBgbzxxht4eHhw5MgRFixYQHV1Nb6+vixduhQ3t9u3Fv3ixYvZsWMHBQUFODs74+TkxNatW5t8rTvK+6Ax+fn5FBQUEBQUZL78VHcG19LJRa/XExUVxa5duwgICAAgIyODkSNHEhMTg1ze8ncd3nnnHfLy8njzzTdxcHAAait0RkdH4+7uzuuvv97iMdQRiUUQBMFKzp8/T3JyMgC9evW6oVomN0qn0xEdHc22bdvM+z1//jwRERFER0ejVCpbLRaRWARBENqRrKwsUlNTMZlM9OjRA19f31aPQSQWQRAEwarEzXtBEATBqkRiEQRBEKxKJBZBEATBqkRiEQRBEKxKJBahTTz66KMMHDiwVQfGEwShdYjEIrS6zMxMEhMTkUgk7Nq1q63DEQTBykRiEVpdXFwcffv25YEHHiAuLs48vbi4mGeffZb+/fszadIkli9fzsMPP2yen5aWxhNPPMGgQYMIDw8nISGhLcIXBKEZYnRjodVt3ryZxx9/nL59+zJlyhQKCgpwc3Nj4cKFqNVq9u3bx6VLl3jqqafMQ3FUVlby5JNP8uKLL/Lpp5+SmprKE088QY8ePczjQgmCcGsQZyxCq0pMTCQrK4t7772XkJAQOnXqRHx8PAaDgR07dvDCCy+gVqvp1q0bEydONK+3e/dufH19mTRpEnK5nF69ehEeHn7bF/MShPZInLEIrSouLo5hw4aZR9wdP3483333HePGjUOv19cb4v3Kny9dusSJEyfMFSKhdlj4CRMmtF7wgiBYRCQWodVotVq+//57jEYjw4YNA2oHzistLaWwsBC5XE5OTg5dunQBqFf50Nvbm4EDB7J27do2iV0QBMuJS2FCq9m5cycymYytW7cSFxdHXFwcCQkJhIWFERcXx5gxY1i5ciVVVVWkpaWxefNm87ojRowgIyODuLg4c92JEydOkJaW1oZHJAhCY0RiEVrNd999x4MPPoiPjw/u7u7mx/Tp09myZQvz58+nrKyMYcOGmWt31w31bW9vz5o1a0hISGD48OHcddddLFu2TPSDEYRbkBjdWLhlLV26lIKCAmJiYto6FEEQroM4YxFuGWlpaZw+fRqTycSJEyf4+uuvGTNmTFuHJQjCdRI374VbRkVFBS+//DJ5eXm4urry5JNPMmrUqLYOSxCE6yQuhQmCIAhWJS6FCYIgCFYlEosgCIJgVSKxCIIgCFYlEosgCIJgVSKxCIIgCFYlEosgCIJgVf8frg8OlDo9wPcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure()\n",
    "fig.set(alpha=0.2)\n",
    "\n",
    "plt.subplot2grid((2,3),(0,0))\n",
    "train.Survived.value_counts().plot(kind='bar')\n",
    "plt.title('Survived')\n",
    "plt.ylabel('num')\n",
    "\n",
    "plt.subplot2grid((2,3),(0,1))\n",
    "train.Pclass.value_counts().plot(kind='bar')\n",
    "plt.title('Pclass')\n",
    "plt.ylabel('num')\n",
    "\n",
    "plt.subplot2grid((2,3),(0,2))\n",
    "plt.scatter(train.Survived,train.Age)\n",
    "plt.ylabel('Age')\n",
    "plt.grid(b=True,which='major',axis='y')\n",
    "plt.title('Age')\n",
    "\n",
    "plt.subplot2grid((2,3),(1,0),colspan=2)\n",
    "train.Age[train.Pclass == 1].plot(kind='kde')\n",
    "train.Age[train.Pclass == 2].plot(kind='kde')\n",
    "train.Age[train.Pclass == 3].plot(kind='kde')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Density')\n",
    "plt.title('Distribution of passenger ages by Pclass')\n",
    "plt.legend(('first','second','third'),loc='best')\n",
    "\n",
    "plt.subplot2grid((2,3),(1,2))\n",
    "train.Embarked.value_counts().plot(kind='bar')\n",
    "plt.title('Embaked')\n",
    "plt.ylabel('num')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pclass      1   2    3\n",
      "Survived              \n",
      "0          80  97  372\n",
      "1         136  87  119\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 查看Survived 与 Pclass的关系\n",
    "Survived_Pclass = train['Pclass'].groupby(train['Survived'])\n",
    "print(Survived_Pclass.value_counts().unstack())\n",
    "\n",
    "Survived_Pclass.value_counts().unstack().plot(kind = 'bar', stacked = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sex       female  male\n",
      "Survived              \n",
      "0             81   468\n",
      "1            233   109\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 查看survived 与 Sex的关系\n",
    "Survived_Sex = train['Sex'].groupby(train['Survived'])\n",
    "print(Survived_Sex.value_counts().unstack())\n",
    "\n",
    "Survived_Sex.value_counts().unstack().plot(kind = 'bar', stacked = True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop('family_size',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 11 columns):\n",
      "PassengerId    418 non-null int64\n",
      "Pclass         418 non-null int64\n",
      "Name           418 non-null object\n",
      "Sex            418 non-null object\n",
      "Age            332 non-null float64\n",
      "SibSp          418 non-null int64\n",
      "Parch          418 non-null int64\n",
      "Ticket         418 non-null object\n",
      "Fare           417 non-null float64\n",
      "Cabin          91 non-null object\n",
      "Embarked       418 non-null object\n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 36.0+ KB\n"
     ]
    }
   ],
   "source": [
    "test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30.272590361445783"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['Age'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['Age']=test['Age'].fillna(test['Age'].mean())\n",
    "test['Fare']=test['Fare'].fillna(test['Fare'].mean())\n",
    "test.drop('Cabin',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 10 columns):\n",
      "PassengerId    418 non-null int64\n",
      "Pclass         418 non-null int64\n",
      "Name           418 non-null object\n",
      "Sex            418 non-null object\n",
      "Age            418 non-null float64\n",
      "SibSp          418 non-null int64\n",
      "Parch          418 non-null int64\n",
      "Ticket         418 non-null object\n",
      "Fare           418 non-null float64\n",
      "Embarked       418 non-null object\n",
      "dtypes: float64(2), int64(4), object(4)\n",
      "memory usage: 32.7+ KB\n"
     ]
    }
   ],
   "source": [
    "test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 11 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            891 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Embarked       891 non-null object\n",
      "dtypes: float64(2), int64(5), object(4)\n",
      "memory usage: 76.6+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
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       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
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       "      <td>C</td>\n",
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       "      <td>3</td>\n",
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       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
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       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Embarked  \n",
       "0      0         A/5 21171   7.2500        S  \n",
       "1      0          PC 17599  71.2833        C  \n",
       "2      0  STON/O2. 3101282   7.9250        S  \n",
       "3      0            113803  53.1000        S  \n",
       "4      0            373450   8.0500        S  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = train.append(test,sort=False)#合并后的数据，方便一起清洗"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
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       "      <th>Name</th>\n",
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       "      <td>1.0</td>\n",
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       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
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       "      <th>4</th>\n",
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       "      <td>0.0</td>\n",
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       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
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       "      <td>0</td>\n",
       "      <td>373450</td>\n",
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       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1       0.0       3   \n",
       "1            2       1.0       1   \n",
       "2            3       1.0       3   \n",
       "3            4       1.0       1   \n",
       "4            5       0.0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Embarked  \n",
       "0      0         A/5 21171   7.2500        S  \n",
       "1      0          PC 17599  71.2833        C  \n",
       "2      0  STON/O2. 3101282   7.9250        S  \n",
       "3      0            113803  53.1000        S  \n",
       "4      0            373450   8.0500        S  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "sexdict = {'male':1, 'female':0}\n",
    "dataset.Sex = dataset.Sex.map(sexdict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      "text/plain": [
       "   PassengerId  Survived  Pclass                     Name  Sex   Age  SibSp  \\\n",
       "0            1       0.0       3  Braund, Mr. Owen Harris    1  22.0      1   \n",
       "\n",
       "   Parch     Ticket  Fare  Embarked_C  Embarked_Q  Embarked_S  \n",
       "0      0  A/5 21171  7.25           0           0           1  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embarked2 = pd.get_dummies(dataset.Embarked, prefix = 'Embarked')\n",
    "\n",
    "dataset = pd.concat([dataset,embarked2], axis = 1) ## 将编码好的数据添加到原数据上\n",
    "dataset.drop(['Embarked'], axis = 1, inplace=True) ## 过河拆桥\n",
    "\n",
    "dataset.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Pclass_3</th>\n",
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      ],
      "text/plain": [
       "   PassengerId  Survived                     Name  Sex   Age  SibSp  Parch  \\\n",
       "0            1       0.0  Braund, Mr. Owen Harris    1  22.0      1      0   \n",
       "\n",
       "      Ticket  Fare  Embarked_C  Embarked_Q  Embarked_S  Pclass_1  Pclass_2  \\\n",
       "0  A/5 21171  7.25           0           0           1         0         0   \n",
       "\n",
       "   Pclass_3  \n",
       "0         1  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pclass = pd.get_dummies(dataset.Pclass, prefix = 'Pclass')\n",
    "\n",
    "dataset = pd.concat([dataset,pclass], axis = 1)\n",
    "dataset.drop(['Pclass'], axis = 1, inplace=True)\n",
    "\n",
    "dataset.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Pclass_1</th>\n",
       "      <th>Pclass_2</th>\n",
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       "      <th>family</th>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived                     Name  Sex   Age  SibSp  Parch  \\\n",
       "0            1       0.0  Braund, Mr. Owen Harris    1  22.0      1      0   \n",
       "\n",
       "      Ticket  Fare  Embarked_C  Embarked_Q  Embarked_S  Pclass_1  Pclass_2  \\\n",
       "0  A/5 21171  7.25           0           0           1         0         0   \n",
       "\n",
       "   Pclass_3  family  \n",
       "0         1       2  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['family']=dataset.SibSp+dataset.Parch+1\n",
    "\n",
    "dataset.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1309 entries, 0 to 417\n",
      "Data columns (total 14 columns):\n",
      "PassengerId    1309 non-null int64\n",
      "Survived       891 non-null float64\n",
      "Sex            1309 non-null int64\n",
      "Age            1309 non-null float64\n",
      "SibSp          1309 non-null int64\n",
      "Parch          1309 non-null int64\n",
      "Fare           1309 non-null float64\n",
      "Embarked_C     1309 non-null uint8\n",
      "Embarked_Q     1309 non-null uint8\n",
      "Embarked_S     1309 non-null uint8\n",
      "Pclass_1       1309 non-null uint8\n",
      "Pclass_2       1309 non-null uint8\n",
      "Pclass_3       1309 non-null uint8\n",
      "family         1309 non-null int64\n",
      "dtypes: float64(3), int64(5), uint8(6)\n",
      "memory usage: 99.7 KB\n"
     ]
    }
   ],
   "source": [
    "dataset.drop(['Ticket','Name'], axis = 1, inplace=True)\n",
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((891, 13), (891,), (418, 13), (418, 2))"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train = dataset.iloc[0:891, :]\n",
    "y_train = x_train.Survived\n",
    "x_train.drop(['Survived'], axis=1, inplace =True)\n",
    "\n",
    "x_test = dataset.iloc[891:, :]\n",
    "x_test.drop(['Survived'], axis=1, inplace =True)\n",
    "\n",
    "y_test = pd.read_csv('../data_files/1.Titantic_data/gender_submission.csv')#测试集\n",
    "y_test=np.squeeze(y_test)\n",
    "\n",
    "x_train.shape,y_train.shape,x_test.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,12))\n",
    "sns.heatmap(dataset.corr(),annot = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((891, 13), (891,), (418, 13), (418, 2))"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train = dataset.iloc[0:891, :]\n",
    "y_train = x_train.Survived\n",
    "x_train.drop(['Survived'], axis=1, inplace =True)\n",
    "\n",
    "x_test = dataset.iloc[891:, :]\n",
    "x_test.drop(['Survived'], axis=1, inplace =True)\n",
    "\n",
    "y_test = pd.read_csv('../data_files/1.Titantic_data/gender_submission.csv')#测试集\n",
    "y_test=np.squeeze(y_test)\n",
    "\n",
    "x_train.shape,y_train.shape,x_test.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.82"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression \n",
    "from sklearn.metrics import accuracy_score\n",
    "model = LogisticRegression()\n",
    "\n",
    "model.fit(x_train.iloc[0:-100,:],y_train.iloc[0:-100])\n",
    "accuracy_score(model.predict(x_train.iloc[-100:,:]),y_train.iloc[-100:].values.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived\n",
       "0          892         0\n",
       "1          893         0\n",
       "2          894         0\n",
       "3          895         0\n",
       "4          896         1"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction1 = model.predict(x_test)\n",
    "result = pd.DataFrame({'PassengerId':y_test['PassengerId'].values, 'Survived':prediction1.astype(np.int32)})\n",
    "result.to_csv(\"../results/predictions1.csv\", index=False)\n",
    "result.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9593301435406698"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test['Survived'], prediction1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9497607655502392"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2 = LogisticRegression()\n",
    "model2.fit(x_train,y_train)\n",
    "\n",
    "prediction2 = model2.predict(x_test)\n",
    "accuracy_score(y_test['Survived'], prediction2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived\n",
       "0          892         0\n",
       "1          893         0\n",
       "2          894         0\n",
       "3          895         0\n",
       "4          896         1"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = pd.DataFrame({'PassengerId':y_test['PassengerId'].values, 'Survived':prediction2.astype(np.int32)})\n",
    "result.to_csv(\"../results/predictions2.csv\", index=False)\n",
    "result.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>variable</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sex</td>\n",
       "      <td>2.669119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Pclass_1</td>\n",
       "      <td>1.086309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Pclass_2</td>\n",
       "      <td>0.586247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SibSp</td>\n",
       "      <td>0.571004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Embarked_C</td>\n",
       "      <td>0.559438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Pclass_3</td>\n",
       "      <td>0.459674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Parch</td>\n",
       "      <td>0.381664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Embarked_Q</td>\n",
       "      <td>0.371561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Embarked_S</td>\n",
       "      <td>0.281883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>family</td>\n",
       "      <td>0.260215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Age</td>\n",
       "      <td>0.030955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Fare</td>\n",
       "      <td>0.004465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PassengerId</td>\n",
       "      <td>0.000328</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       variable  importance\n",
       "1           Sex    2.669119\n",
       "9      Pclass_1    1.086309\n",
       "10     Pclass_2    0.586247\n",
       "3         SibSp    0.571004\n",
       "6    Embarked_C    0.559438\n",
       "11     Pclass_3    0.459674\n",
       "4         Parch    0.381664\n",
       "7    Embarked_Q    0.371561\n",
       "8    Embarked_S    0.281883\n",
       "12       family    0.260215\n",
       "2           Age    0.030955\n",
       "5          Fare    0.004465\n",
       "0   PassengerId    0.000328"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat((pd.DataFrame(x_train.columns, columns = ['variable']), \n",
    "           pd.DataFrame(abs(model.coef_[0]), columns = ['importance'])), \n",
    "           axis = 1).sort_values(by='importance', ascending = False)[:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.81005587, 0.79775281, 0.78651685, 0.76966292, 0.8258427 ])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score, train_test_split\n",
    "cross_val_score(model2, x_train, y_train, cv=5,scoring='accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.85"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(model2.predict(x_train.iloc[-100:,:]),y_train.iloc[-100:].values.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>variable</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sex</td>\n",
       "      <td>2.618185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Pclass_1</td>\n",
       "      <td>1.367680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Embarked_C</td>\n",
       "      <td>0.702703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SibSp</td>\n",
       "      <td>0.646847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Pclass_2</td>\n",
       "      <td>0.582498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Pclass_3</td>\n",
       "      <td>0.578508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Embarked_Q</td>\n",
       "      <td>0.447769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Parch</td>\n",
       "      <td>0.400804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>family</td>\n",
       "      <td>0.324019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Embarked_S</td>\n",
       "      <td>0.221198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Age</td>\n",
       "      <td>0.036946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Fare</td>\n",
       "      <td>0.002824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PassengerId</td>\n",
       "      <td>0.000084</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       variable  importance\n",
       "1           Sex    2.618185\n",
       "9      Pclass_1    1.367680\n",
       "6    Embarked_C    0.702703\n",
       "3         SibSp    0.646847\n",
       "10     Pclass_2    0.582498\n",
       "11     Pclass_3    0.578508\n",
       "7    Embarked_Q    0.447769\n",
       "4         Parch    0.400804\n",
       "12       family    0.324019\n",
       "8    Embarked_S    0.221198\n",
       "2           Age    0.036946\n",
       "5          Fare    0.002824\n",
       "0   PassengerId    0.000084"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat((pd.DataFrame(x_train.columns, columns = ['variable']), \n",
    "           pd.DataFrame(abs(model2.coef_[0]), columns = ['importance'])), \n",
    "           axis = 1).sort_values(by='importance', ascending = False)[:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_param_grid={'C':[0.01,0.1,0.2,1,2,10],'penalty':['l1','l2'],'solver':['liblinear'], 'tol':[1e-6]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "grid=GridSearchCV(LogisticRegression(),param_grid=lr_param_grid,cv=5,scoring='accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(LogisticRegression(C=1, class_weight=None, dual=False, fit_intercept=True,\n",
       "                    intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                    multi_class='auto', n_jobs=None, penalty='l2',\n",
       "                    random_state=None, solver='liblinear', tol=1e-06, verbose=0,\n",
       "                    warm_start=False),\n",
       " 0.79686774213797,\n",
       " {'C': 1, 'penalty': 'l2', 'solver': 'liblinear', 'tol': 1e-06})"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.fit(x_train, y_train)\n",
    "grid.best_estimator_,grid.best_score_,grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9497607655502392"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction1 = grid.best_estimator_.predict(x_test)\n",
    "accuracy_score(y_test['Survived'], prediction1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8038277511961722"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 随机森林\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "model3 = RandomForestClassifier(n_estimators=500, criterion='entropy', max_depth=5, min_samples_split=1.0,\n",
    "                                min_samples_leaf=1, max_features='auto',    bootstrap=False, oob_score=False, n_jobs=1, random_state=0,\n",
    "                                verbose=0)\n",
    "model3.fit(x_train, y_train)\n",
    "prediction3 =  model3.predict(x_test)\n",
    "\n",
    "result = pd.DataFrame({'PassengerId':y_test['PassengerId'].values, 'Survived':prediction3.astype(np.int32)})\n",
    "result.to_csv(\"../results/predictions3.csv\", index=False)\n",
    "accuracy_score(y_test['Survived'], prediction3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7966507177033493"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "model4 = DecisionTreeClassifier(criterion='entropy', max_depth=7, min_impurity_decrease=0.0)\n",
    "model4.fit(x_train, y_train)\n",
    "prediction4 = model4.predict(x_test)\n",
    "\n",
    "result = pd.DataFrame({'PassengerId':y_test['PassengerId'].values, 'Survived':prediction4.astype(np.int32)})\n",
    "result.to_csv(\"../results/predictions4.csv\", index=False)\n",
    "accuracy_score(y_test['Survived'], prediction4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Vote</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Vote  Survived\n",
       "0          892     0     False\n",
       "1          893     0     False\n",
       "2          894     0     False\n",
       "3          895     0     False\n",
       "4          896     2      True"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediciton_vote = pd.DataFrame({'PassengerId': y_test['PassengerId'],\n",
    "                                 'Vote': prediction1.astype(int)+prediction2.astype(int)+prediction3.astype(int)})\n",
    "\n",
    "vote = { 0:False,1:False,2:True,3:True}\n",
    "\n",
    "prediciton_vote['Survived']=prediciton_vote['Vote'].map(vote)\n",
    "\n",
    "prediciton_vote.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9497607655502392"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test['Survived'], prediciton_vote['Survived'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = pd.DataFrame({'PassengerId':y_test['PassengerId'].values, 'Survived':prediciton_vote.Survived.astype(np.int32)})\n",
    "result.to_csv(\"../results/predictions5.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import metrics\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "import seaborn as sns  \n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "train = pd.read_csv('../data_files/1.Titantic_data/train.csv')\n",
    "test = pd.read_csv('../data_files/1.Titantic_data/test.csv')\n",
    "\n",
    "train['Age']=train['Age'].fillna(train['Age'].mean()) # 用平均值填充\n",
    "train.drop(['Cabin'],axis=1,inplace=True) # 删去Cabin的那一列数据\n",
    "train.Embarked = train.Embarked.fillna('S') # 用’S'填补缺失值\n",
    "\n",
    "test['Age']=test['Age'].fillna(test['Age'].mean())\n",
    "test['Fare']=test['Fare'].fillna(test['Fare'].mean())\n",
    "test.drop('Cabin',axis=1,inplace=True)\n",
    "\n",
    "dataset = train.append(test,sort=False)#合并后的数据，方便一起清洗\n",
    "\n",
    "sexdict = {'male':1, 'female':0}\n",
    "dataset.Sex = dataset.Sex.map(sexdict)\n",
    "\n",
    "embarked2 = pd.get_dummies(dataset.Embarked, prefix = 'Embarked')\n",
    "\n",
    "dataset = pd.concat([dataset,embarked2], axis = 1) ## 将编码好的数据添加到原数据上\n",
    "dataset.drop(['Embarked'], axis = 1, inplace=True) ## 过河拆桥\n",
    "\n",
    "pclass = pd.get_dummies(dataset.Pclass, prefix = 'Pclass')\n",
    "\n",
    "dataset = pd.concat([dataset,pclass], axis = 1)\n",
    "dataset.drop(['Pclass'], axis = 1, inplace=True)\n",
    "\n",
    "dataset['family']=dataset.SibSp+dataset.Parch+1\n",
    "\n",
    "dataset.drop(['Ticket','Name'], axis = 1, inplace=True)\n",
    "x_train = dataset.iloc[0:891, :]\n",
    "y_train = x_train.Survived\n",
    "x_train.drop(['Survived'], axis=1, inplace =True)\n",
    "\n",
    "x_test = dataset.iloc[891:, :]\n",
    "x_test.drop(['Survived'], axis=1, inplace =True)\n",
    "\n",
    "y_test = pd.read_csv('../data_files/1.Titantic_data/gender_submission.csv')#测试集\n",
    "y_test=np.squeeze(y_test)\n",
    "\n",
    "x_train.shape,y_train.shape,x_test.shape, y_test.shape\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression \n",
    "from sklearn.metrics import accuracy_score\n",
    "model = LogisticRegression()\n",
    "\n",
    "model.fit(x_train.iloc[0:-100,:],y_train.iloc[0:-100])\n",
    "accuracy_score(model.predict(x_train.iloc[-100:,:]),y_train.iloc[-100:].values.reshape(-1,1))\n",
    "\n",
    "prediction1 = model.predict(x_test)\n",
    "result = pd.DataFrame({'PassengerId':y_test['PassengerId'].values, 'Survived':prediction1.astype(np.int32)})\n",
    "result.to_csv(\"../results/predictions1.csv\", index=False)\n",
    "\n",
    "model2 = LogisticRegression()\n",
    "model2.fit(x_train,y_train)\n",
    "\n",
    "prediction2 = model2.predict(x_test)\n",
    "\n",
    "result = pd.DataFrame({'PassengerId':y_test['PassengerId'].values, 'Survived':prediction2.astype(np.int32)})\n",
    "result.to_csv(\"../results/predictions2.csv\", index=False)\n",
    "\n",
    "# 随机森林\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "model3 = RandomForestClassifier(n_estimators=500, criterion='entropy', max_depth=5, min_samples_split=1.0,\n",
    "                                min_samples_leaf=1, max_features='auto',    bootstrap=False, oob_score=False, n_jobs=1, random_state=0,\n",
    "                                verbose=0)\n",
    "model3.fit(x_train, y_train)\n",
    "prediction3 =  model3.predict(x_test)\n",
    "\n",
    "result = pd.DataFrame({'PassengerId':y_test['PassengerId'].values, 'Survived':prediction3.astype(np.int32)})\n",
    "result.to_csv(\"../results/predictions3.csv\", index=False)\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "model4 = DecisionTreeClassifier(criterion='entropy', max_depth=7, min_impurity_decrease=0.0)\n",
    "model4.fit(x_train, y_train)\n",
    "prediction4 = model4.predict(x_test)\n",
    "\n",
    "result = pd.DataFrame({'PassengerId':y_test['PassengerId'].values, 'Survived':prediction4.astype(np.int32)})\n",
    "result.to_csv(\"../results/predictions4.csv\", index=False)\n",
    "\n",
    "prediciton_vote = pd.DataFrame({'PassengerId': y_test['PassengerId'],\n",
    "                                 'Vote': prediction1.astype(int)+prediction2.astype(int)+prediction3.astype(int)})\n",
    "vote = { 0:False,1:False,2:True,3:True}\n",
    "\n",
    "prediciton_vote['Survived']=prediciton_vote['Vote'].map(vote)\n",
    "\n",
    "prediciton_vote.head()\n",
    "\n",
    "result = pd.DataFrame({'PassengerId':y_test['PassengerId'].values, 'Survived':prediciton_vote.Survived.astype(np.int32)})\n",
    "result.to_csv(\"../results/predictions5.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import metrics\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "import seaborn as sns  \n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "train = pd.read_csv('../data_files/1.Titantic_data/train.csv')\n",
    "test = pd.read_csv('../data_files/1.Titantic_data/test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "fig.set(alpha=0.2)  # 设定图表颜色alpha参数\n",
    "\n",
    "Survived_cabin = train.Survived[pd.notnull(train.Cabin)].value_counts()\n",
    "Survived_nocabin = train.Survived[pd.isnull(train.Cabin)].value_counts()\n",
    "df=pd.DataFrame({u'有':Survived_cabin, u'无':Survived_nocabin}).transpose()\n",
    "df.plot(kind='bar', stacked=True)\n",
    "plt.title(u\"按Cabin有无看获救情况\")\n",
    "plt.xlabel(u\"Cabin有无\") \n",
    "plt.ylabel(u\"人数\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Age']=train['Age'].fillna(train['Age'].mean()) # 用平均值填充\n",
    "train.drop(['Cabin'],axis=1,inplace=True) # 删去Cabin的那一列数据\n",
    "train.Embarked = train.Embarked.fillna('S') # 用’S'填补缺失值\n",
    "\n",
    "test['Age']=test['Age'].fillna(test['Age'].mean())\n",
    "test['Fare']=test['Fare'].fillna(test['Fare'].mean())\n",
    "test.drop('Cabin',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dummies_Cabin = pd.get_dummies(train['Cabin'],prefix='Cabin')\n",
    "\n",
    "# dummies_Embarked = pd.get_dummies(train['Embarked'],prefix='Embarked')\n",
    "\n",
    "# dummies_Sex = pd.get_dummies(train['Sex'],prefix='Sex')\n",
    "\n",
    "# dummies_Pclass = pd.get_dummies(train['Pclass'],prefix='Pclass')\n",
    "\n",
    "# train = pd.concat([train,dummies_Cabin,dummies_Embarked,dummies_Sex,dummies_Pclass],axis=1)\n",
    "# train.drop(['Pclass', 'Name', 'Sex', 'Ticket', 'Cabin', 'Embarked'], axis=1, inplace=True)\n",
    "# train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = train.append(test,sort=False)#合并后的数据，方便一起清洗\n",
    "\n",
    "# dataset.loc[(dataset.Cabin.notnull()),'Cabin'] = 'Yes'\n",
    "# dataset.loc[(dataset.Cabin.isnull()),'Cabin'] = 'No'\n",
    "\n",
    "# cabin = pd.get_dummies(dataset.Cabin,prefix='Cabin')\n",
    "# dataset.drop(['Cabin'],axis=1,inplace=True)\n",
    "# dataset = pd.concat([dataset,cabin], axis = 1)\n",
    "\n",
    "sexdict = {'male':1, 'female':0}\n",
    "dataset.Sex = dataset.Sex.map(sexdict)\n",
    "\n",
    "embarked2 = pd.get_dummies(dataset.Embarked, prefix = 'Embarked')\n",
    "\n",
    "dataset = pd.concat([dataset,embarked2], axis = 1) ## 将编码好的数据添加到原数据上\n",
    "dataset.drop(['Embarked'], axis = 1, inplace=True) ## 过河拆桥\n",
    "\n",
    "pclass = pd.get_dummies(dataset.Pclass, prefix = 'Pclass')\n",
    "\n",
    "dataset = pd.concat([dataset,pclass], axis = 1)\n",
    "dataset.drop(['Pclass'], axis = 1, inplace=True)\n",
    "\n",
    "dataset['family']=dataset.SibSp+dataset.Parch+1\n",
    "\n",
    "dataset.drop(['Ticket','Name'], axis = 1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((891, 13), (891,), (418, 13), (418, 2))"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train = dataset.iloc[0:891, :]\n",
    "y_train = x_train.Survived\n",
    "x_train.drop(['Survived'], axis=1, inplace =True)\n",
    "\n",
    "x_test = dataset.iloc[891:, :]\n",
    "x_test.drop(['Survived'], axis=1, inplace =True)\n",
    "\n",
    "y_test = pd.read_csv('../data_files/1.Titantic_data/gender_submission.csv')#测试集\n",
    "y_test=np.squeeze(y_test)\n",
    "\n",
    "x_train.shape,y_train.shape,x_test.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "clf = DecisionTreeClassifier(criterion='entropy')# 选择ID3算法，即用entropy衡量纯度\n",
    "\n",
    "clf = clf.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted = clf.predict(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy_score: 1.0\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "acc = accuracy_score(y_train,predicted)#或者直接使用clf.score\n",
    "print('accuracy_score:',acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[549,   0],\n",
       "       [  0, 342]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "m = confusion_matrix(y_train,predicted)\n",
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       1.00      1.00      1.00       549\n",
      "         1.0       1.00      1.00      1.00       342\n",
      "\n",
      "    accuracy                           1.00       891\n",
      "   macro avg       1.00      1.00      1.00       891\n",
      "weighted avg       1.00      1.00      1.00       891\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_train,predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.66666667, 0.79775281, 0.75280899, 0.62921348, 0.8988764 ,\n",
       "       0.71910112, 0.83146067, 0.83146067, 0.78651685, 0.84269663])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "clf = DecisionTreeClassifier(criterion='entropy')\n",
    "clf.fit(x_train,y_train)\n",
    "\n",
    "from sklearn.model_selection import cross_val_score\n",
    "acc = cross_val_score(clf,x_train,y_train,cv=10)\n",
    "acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7756554307116105\n"
     ]
    }
   ],
   "source": [
    "print(acc.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.22475793, 0.22656221, 0.18600592, 0.02783334, 0.01414729,\n",
       "       0.16605715, 0.00392361, 0.01121017, 0.        , 0.00467295,\n",
       "       0.01464706, 0.08344428, 0.0367381 ])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  0,  2,  5, 11, 12,  3, 10,  4,  7,  9,  6,  8])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.feature_importances_.argsort()[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Sex', 'PassengerId', 'Age', 'Fare', 'Pclass_3', 'family', 'SibSp',\n",
       "       'Pclass_2', 'Parch', 'Embarked_Q', 'Pclass_1', 'Embarked_C',\n",
       "       'Embarked_S'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.columns[clf.feature_importances_.argsort()[::-1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "importances = clf.feature_importances_\n",
    "names = x_train.columns\n",
    "plt.title('Features Importance')\n",
    "plt.bar(range(0,len(names)),importances[importances.argsort()[::-1]])\n",
    "plt.xticks(range(0,len(names)),names[importances.argsort()[::-1]],rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_features = x_train\n",
    "train_labels = y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import tree\n",
    "graphtree = tree.export_graphviz(clf,out_file='tree.dot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree.plot_tree(clf);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pydotplus\n",
    "with open('tree.dot', 'w') as f:\n",
    " \n",
    "    dot_data = tree.export_graphviz(clf, out_file=None)\n",
    " \n",
    "    f.write(dot_data)\n",
    " \n",
    " \n",
    "#画图方法2-生成pdf文件\n",
    " \n",
    "dot_data = tree.export_graphviz(clf, out_file=None,feature_names=clf.feature_importances_,\n",
    " \n",
    "filled=True, rounded=True, special_characters=True)\n",
    " \n",
    "graph = pydotplus.graph_from_dot_data(dot_data)\n",
    " \n",
    "###保存图像到pdf文件\n",
    " \n",
    "graph.write_pdf(\"treetwo.pdf\")\n",
    " "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
